735349 Water Tree Cable Joints
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Pattern Recognition and Knowledge Extraction for On-line Partial Discharge Monitoring with Defect Location
PROEFSCHRIFT
ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag van de rector magni cus, prof.dr.ir. C.J. van Duijn, voor een commissie aangewezen door het College voor Promoties in het openbaar te verdedigen op woensdag 5 september 2012 om 14.00 uur
door
Shima Mousavi Gargari
geboren te Teheran, Iran
Dit proefschrift is goedgekeurd door de promotor:
prof.dr.ir. E.F. Steennis
Copromotor: dr. P.A.A.F. Wouters
A catalogue record is available from the Eindhoven University of Technology Library. ISBN: 978-90-386-3209-4
To my parents ,
Nahid and Kazem Mousavi For providing me the opportunity to live my life in the safest place ever which has been enlightened with their glorious love and will remain the only sweet home for me to the end of my life, for showing me the beauty of the life with an exceptional kindness that they carry in their hearts, for lifting me up to reach all my dreams, and always being proud of me regardless of how less I achieved, for teaching me the true meaning of freedom and giving me wings to y and to not be imprisoned in the cage of steadiness, for teaching me to enjoy working hard and trying my best to achieve whatever I want, but never regret if I can't obtain it, for teaching me to be strong, kind, patient and tolerant to feel the joy and happiness in the world, for embracing me when I am scared and making me smile, for comforting all my pains and wiping up all the tears I shed, for holding me safe whenever I stand sharp to the edge, for holding me up whenever I fall deep down, for teaching me to experience the pleasure in forgiveness with all my heart regardless of how bad I get hurt, for making me to think about others and being compassionate and above all for being the shoulders that I can always lean on even if I am living far away from them
and to:
Fred Steennis whom I don't want to only call a promoter For supporting me through all the ups and downs which I experienced during the years of being his student, for showing me that the good and great souls still exist in this harsh world, for returning me back my faith when I fully lost it, for bringing me back the deep smile used to cover my face and I lost it once, and at the end for being a real human being and a real someone. To the end of my life, I remain grateful and respectful with all my heart and my being to these three special people of my little world and I hope to get an opportunity to return them back even though partly whatever of friendship, love, kindness, and support they have extended to me. May God give them a long blessed life and keep them safe for me.
Promoter: prof.dr.ir. E.F. Steennis Copromoter: dr. P.A.A.F. Wouters
Core committee members: prof.dr.ir. J.G. Slootweg (Technische Universiteit Eindhoven) prof.dr. J.J. Smit (Technische Universiteit Delft ) dr.ir. P.C.J.M. van der Wielen (DNV KEMA)
Other doctoral committee members: prof.dr.ir. A.C.P.M. Backx (Chairman - Technische Universiteit Eindhoven) prof.dr.ir. M.H.J. Bollen (Luleå University of Technology) ir. M.J.M. van Riet (Liandon)
Summary Providing su cient reliability is one of the main issues for the electrical power society. Failure statistics reveal that electrical distribution systems constitute the largest risk on interrupted supply of electrical power. In fact the majority of the breakdowns in MV grids occurs due to the failure of components within cable systems, especially the cable joints. Mostly, the aging mechanisms leading to failure in cable networks are associated with partial discharge (PD) activity. The present thesis is concerned with the translation of the PD activity measured on a large number of live medium voltage circuits, to parameters which express the actual condition of the cable system. The analysis is applied to data obtained from the SCG (Smart Cable Guard) system which is operational since 2007 mainly in Dutch networks and some abroad. Good understanding of the degradation mechanisms helps in selecting the right characteristics to identify the state of the insulation. In this thesis, most common defects in the cable system components, their related degradation mechanisms and the possible root causes are reported. Basically, insulation breakdowns are caused by degradation processes initiated by induced defects in the insulation.
These
defects which have their initiatory site either in the insulation itself or in the other sections in contact with the insulation e.g. conductors, are basically a result of:
Bad design with an inadequate design testing Wrong production and inadequate production testing Mistakes during transportation and installation Extreme operational conditions Aggressive environmental conditions, excavation damages Normal operational conditions over a long period of time
Defect inducing factors classi ed as manufacturing, handling, and in-service factors are introduced in this thesis as well. The study of the degradation mechanisms, as presented in this thesis, shows that most of the deteriorating processes take place for PILC cable, XLPE cables, and accessories lead to initiation of PDs. Therefore, v
vi
emphasizing the necessity of monitoring PDs. Characteristic parameters are de ned based on the PD data. These parameters are PD occurrence rate (number of PDs in one hour) and PD charge density (involved charge of the PDs in one hour). The choice of one hour is found adequate for the present operation of the SCG. The occurrence rate and charge density parameters are normalized as per power cycle and per mille cable length to accommodate comparison for other durations and between cables with dierent length. Besides these average values, for both parameters maximum values within each hour are taken as well. In addition, the distribution of PD magnitudes is monitored, however, over a longer time scale to get su cient statistical signi cance. It is found that the Weibull distribution is capable to make distinction between potential harmful internal PDs and corona discharges. For all de ned quantities, their change over time is the most indicative to observe progressing insulation degradation. The fast growth in the number of installed SCG units in the eld makes it virtually impossible to individually investigate all patterns for each circuit. During the course of this research, a so-called decision support tool to automate the defect identi cation was developed. The tool attempts to detect concentrations over length and over time. In rst stage the amount of noise is reduced, especially for circuits where the SCG settings were such that a broad background is observed. For the concentration detection two techniques were applied. tions were applied to t local concentration.
Normal distribu-
R-squared test used to determine
the statistical signi cance showed a limitation of this method when PD concentrations only cover a few samples in the records. Alternatively, a sequence clustering approach was developed.
The clustering algorithm aims to detect the activities
in close vicinity of a potential defective site and combine them as a cluster both over length and time. The detected clusters and their related representative values form the basis to identify the real defect in the insulation and the involved failure risk.
A risk index should be assigned to detected concentrations to express the
expected severity. In this research the risk index is based on the average charge density and occurrence rate per detected cluster, as well as the cluster width and the observed trend. The logistic function is applied to evaluate the actual values of these parameters compared to component speci c critical values.
These risk
indices then are combined to form an overall risk parameter. The decision support tool is applied to the data obtained through installed SCG units which are continuously monitoring the condition of over 100 live circuits. The tool managed to identify the defective sites and giving valid warnings, however, invalid warnings were also recorded which all together resulted to the performance of about 63 %. Optimization of the tool after nalizing this work has resulted in an increase of the performance of the risk assessment to 84%.
Samenvatting Hoge betrouwbaarheid is één van de hoofddoelstellingen binnen de energievoorziening.
Uit faalstatistieken blijkt, dat vooral het distributienet bijdraagt aan het
risico op onderbreking van elektrische energielevering. Het merendeel van fouten in het middenspanningsnet is toe te schrijven aan componenten binnen een vermogenskabelverbinding, in het bijzonder aan falen van kabelmoen. Degradatie in kabelverbindingen t.g.v. verouderingsprocessen gaat veelal gepaard met Partiële Ontladingen (PD, Partial Discharges).
Dit proefschrift richt zich op de vertaal-
slag van PD activiteit, waargenomen in een groot aantal in bedrijf zijnde middenspanningsnetten, naar parameters die de actuele conditie van die kabelsystemen aangeven.
De analyse is toegepast op gegevens die zijn verkregen met het
SCG (Smart Cable Guard) systeem.
Dit systeem is sedert 2007 operationeel,
voornamelijk in circuits binnen Nederland maar ook enkele daarbuiten. Selectie van geschikte parameters om de conditie van de isolatie te kenmerken vereist een goed begrip van het degradatieproces. Dit proefschrift geeft een overzicht van de meest voorkomende defecten in componenten in een kabelverbinding, de daarmee geassocieerde degradatieprocessen en de onderliggende oorzaken. Doorslag van isolatie wordt veroorzaakt door verouderingsprocessen geïnduceerd door gebreken in de elektrische isolatie.
Deze gebreken hebben hun oorsprong ofwel in
de isolatie zelf ofwel in de directe nabijheid, bijvoorbeeld in de geleiders. Ze zijn globaal in te delen als:
Slecht ontwerp in combinatie met gebrekkig testen Productiefouten en ontoereikende controle Fouten tijdens transport en installatie Extreme belastingscondities Agressieve milieuomstandigheden of schade bij graafwerkzaamheden Normaal gebruik over een lange tijdsperiode
Dit proefschrift maakt tevens onderscheid in defecten ontstaan tijdens productie, verwerking of bedrijf.
Het merendeel van verouderingsprocessen voorkomend in vii
viii
PILC kabels, XLPE kabels en in accessoires voor beide isolatietypen geven aanleiding tot PD activiteit. Dit onderstreept het belang van PD diagnostiek. Uit de gemeten ruwe PD-gegevens worden karakteristieke parameters onttrokken. Deze parameters zijn: PD dichtheid (aantal PDs gemeten over een uur) en PD ladingsdichtheid (geaccumuleerde PD lading in een uur). De keuze hier voor een tijdsduur van een uur hangt samen met de wijze waarop het SCG systeem momenteel wordt bedreven. Beide parameters worden genormeerd per periode van de bedrijfsspanning en per promille kabellengte, zodat vergelijk van verschillende kabelcircuits direct mogelijk is. Ook maximale waarden voorkomend binnen ieder uur worden geëvalueerd. De statistische verdeling van de PD amplitudes is voor de analyse geaccumuleerd over een langer tijdsbestek voor voldoende statistische signi cantie.
Het blijkt dat gebruik van de Weibull-verdeling ons in staat stelt
onderscheid te maken tussen potentieel schadelijke interne PDs en externe corona ontladingen.
Voor alle gede nieerde grootheden is het verloop daarin indicatief
voor de ontwikkeling van een defect. De snelle groei in het aantal geïnstalleerde SCG eenheden maakt het vrijwel onmogelijk om alle individuele patronen voor elk circuit handmatig te analyseren. In dit onderzoek, is geautomatiseerde defect identi catie onderzocht. Het gekozen algoritme probeert PD concentraties te ontdekken. In een eerste stap wordt het ruisaandeel verminderd, vooral voor circuits waar de SCG systeem zodanig is ingesteld, dat een brede achtergrond wordt waargenomen. Voor detectie van concentraties zijn twee methoden bekeken.
De eerste methode is gebaseerd op het
tten d.m.v.
een normaalverdeling aan de verdeling van iedere gemeten lokale
concentratie.
In geval van concentraties die slechts enkele samples in een met-
ing omvatten is statistische signi cantie volgens een R-kwadraat test aan de t laag. Een tweede methode tracht PDs in elkanders nabijheid op te sporen, zowel in plaats als in tijd.
Deze PDs worden gecombineerd tot een cluster en als een
potentieel defect geclassi ceerd. De gedetecteerde clusters vormen de basis voor risicoanalyse voor falen van de elektrische isolatie. Aan de gedetecteerde concentraties wordt een risico-index toegewezen als inschatting van hun ernst.
In dit
onderzoek is de risico-index gebaseerd op gemiddelde waarden van PD dichtheid en ladingsdichtheid in de gedetecteerde clusters Daarnaast zijn de clusterbreedte en de waargenomen trends geëvalueerd.
De evaluatie gebeurt aan de hand van
de logistieke functie, waarmee de werkelijke waarden van genoemde parameters gerelateerd worden aan component speci eke kritieke waarden. De verkregen indexcijfers worden vervolgens gecombineerd tot een algehele risicoparameter. De risico-indexering is toegepast op de gegevens die zijn verkregen uit geïnstalleerde SCG eenheden die gezamenlijk ruim honderd circuits bewaken.
Het
algoritme slaagt erin veel van de geconstateerde defecten correct te herkennen. In diverse situaties is ook een hoge risico-index toegekend aan concentraties die minder urgent bleken te zijn.
Al met al is een scoringsprestatie van ongeveer
63% bereikt. Verdere optimalisatie van de methode, mede op basis van gegevens
ix
verkregen na het tot stand komen van dit proefschrift, resulteerde in een score van 84%.
Contents Contents 1
2
x
Introduction
1
1.1
Medium voltage power cable system
1.2
Maintenance strategies
1.3
Research Objective
1.4
. . . . . . . . . . .
3
. . . . . . . . . . . . . . . . . . . .
8
Thesis outline . . . . . . . . . . . . . . . . . . . . . . . .
8
Defects and degradation mechanism in MV cable insulation systems
3
4
5
6
3
. . . . . . . . . . . . . . . . . .
11
2.1
Defects in MV cable insulation system . . . . . . . . . .
12
2.2
Degradation mechanism
. . . . . . . . . . . . . . . . . .
16
2.3
Root causes
. . . . . . . . . . . . . . . . . . . . . . . .
21
Partial discharge monitoring
25
3.1
PD activity and aging
. . . . . . . . . . . . . . . . . . .
25
3.2
Discharge magnitude . . . . . . . . . . . . . . . . . . . .
29
3.3
PD diagnostics: on-line versus o-line
. . . . . . . . . .
31
3.4
PD diagnostic tool - Smart cable guard
. . . . . . . . .
33
Partial discharge pattern recognition
37
4.1
Data mining and pattern recognition . . . . . . . . . . .
37
4.2
PD related parameters . . . . . . . . . . . . . . . . . . .
38
4.3
PD related patterns
. . . . . . . . . . . . . . . . . . . .
41
4.4
Statistical parameters for PD patterns . . . . . . . . . .
44
Decision Support System for Smart Cable Guard
53
5.1
Noise Reduction Algorithm
. . . . . . . . . . . . . . . .
53
5.2
Defect identi er . . . . . . . . . . . . . . . . . . . . . . .
63
5.3
Risk Indexing . . . . . . . . . . . . . . . . . . . . . . . .
72
5.4
Performance Index . . . . . . . . . . . . . . . . . . . . .
77
Evaluation of the automated defect detection algorithms
79 x
CONTENTS
7
xi
6.1
Controlled measurement . . . . . . . . . . . . . . . . . .
79
6.2
Application to eld data
86
6.3
Performance of the automated defect detection
. . . . . . . . . . . . . . . . . . . . . .
Conclusion and Recommendation 7.1
Conclusion
7.2
Recommendations
99
105
. . . . . . . . . . . . . . . . . . . . . . . . . 105 . . . . . . . . . . . . . . . . . . . . . 107
A Weibull Model
109
A.1
Maximum Likelihood Estimates (MLE): . . . . . . . . . 110
A.2
Con dence interval . . . . . . . . . . . . . . . . . . . . . 111
B Overview of the results obtained by SCG system
113
Bibliography
119
List of abbreviations and symbols
127
List of Publication
133
Acknowledgement
135
Curriculum Vitae
139
Chapter 1
Introduction Reliable electric energy supply from its generation via transmission and distribution systems up to delivery to the consumers, is one of the main issues for the electrical power society. During recent decades electrical power systems have undergone major changes, due to so-called deregulation or liberalization. On transmission level, the systems are nowadays operated by the transmission system operators (TSOs) and transmit energy from generation to the regional distribution networks through the main power lines. On distribution level, the networks are owned and operated by the utilities. Before the restructuring (privatization, deregulation) of the electricity market, the transmission and distribution networks in many countries (e.g.
France and Great Britain) were often owned by a single
national rm which also owned most of the production units and which, next to operating the network, was also responsible for preserving the network security, maintaining the systems and providing su cient reliability. Where it concerns reliability there is a fundamental dierence in philosophy between transmission and distribution. Interruptions originating in the transmission system, impact many customers often over a large geographical area (this is often called a "black-out"), whereas interruptions originating within the distribution networks have only local consequences. Therefore, most capital investment and the highest attention were allocated to the transmission network, which would cause more serious problems in case of a failure.
This has been a great success
because the number of large interruptions (blackouts) has to some extent limited (the last major incidents in the European transmission system took place in 2003 and 2006). After privatization of the electricity market, separate parties became responsible for operating the transmission and distribution networks.
Utilities
(distribution network operators) have their own budget to invest on improving the reliability and quality of the power delivery to their low-voltage customers. Besides, the transmission systems are highly reliable owing to all eorts done on preserving them. The majority of interruptions are now due to incidents in the distribution network. To further enhance the system reliability, to better supply an increasing number of customers, improvements at distribution level are required. As a result, issues concerning distribution networks are getting more attention in 1
2
CHAPTER 1.
INTRODUCTION
research and development. Moreover, the introduction of the distributed generation (DG) to the traditional electric power system has further brought attention to the distribution network. The reliability of the distribution networks has become an important issue and consequently the focus is moving from transmission to distribution network. Failure statistics reveal that electrical distribution systems constitute the largest risk on interrupted supply of electrical power [1].
For instance, more than 80%
[2] of the supply interruption of the MV customers in Germany is caused by the failure of the components in MV networks and almost 75% of mean outage time per customer in the Netherlands is caused by failures in MV cable systems [3, 4]. From technical and environmental point of views, modern societies' demands have forced the power networks to undergo key infrastructural changes. Overhead lines tend to be replaced by underground power cables. Cables are not only from a visual point of view overtaking especially in the densely populated areas. Overhead lines, though cheaper, are more vulnerable to environmental impact such as adverse weather condition which makes cables a reliable alternative. For instance, in the Netherlands almost 100% of the distribution network comprises cables at the MV level with a total length exceeding 100 000 km [5].
The prominent percentage
of the cable length in the Netherlands is still paper insulated lead covered cables mainly due to the fact that most of the Dutch cable network was installed before the polyethylene cables were available.
However, polyethylene insulated cables,
being introduced in distribution systems since the 1970's, are presently taking over. In countries where the electricity networks were introduced during and after the 70's, polyethylene insulated cables are dominating the paper insulated cables and even in some cases no paper insulated cables are installed. Table 1.1, illustrates the failure statistics in MV grid recorded by one of the Dutch utilities [3] in 2001, where a fraction of 59% of the outages in MV grid is caused by failures in cables, joints and terminations.
These statistics underline
the importance of maintenance of the MV cable connections.
Table 1.1:
Failure statistics in MV grid
[3]
Failure caused by
Percentage
Cable circuits:
59%
Digging activities
17%
MV switchgear
4%
MV transformers
2%
Cables Joints Terminations
26% 31% 2%
Secondary installation
4%
Others and unknown
14%
1.1.
MEDIUM VOLTAGE POWER CABLE SYSTEM
3
1.1 Medium voltage power cable system The distribution network, which carries the electrical energy from the substations to the customers, refers to any voltage level below 36kV including medium voltage (MV) feeders (1kV-35kV) and low voltage (LV) feeders (< 1kV). At the medium voltage level, generally, both underground cables and overhead lines are used in the network.
Underground power cables tend to be an appealing alternative to
overhead lines. However, replacing distribution overhead lines with underground power cables on large scale includes economical, technical and societal aspects. In urban regions MV feeders consist of underground power cables. The feeder does not only consist of cable but also it includes interconnecting units which are referred to as ring main units (RMUs). Basically, a cable between two consecutive RMUs can have a length up to 7 km and comprises shorter cable segments connected by joints and two terminations at the ends. Apart from the insulation of the cable itself, the insulation of the other sub-components, joints and terminations, can fail. Although, the cable insulation itself, due to its volume, could be expected to be dominant for potential damages; in practice, cable joints form the most fragile components in the MV cable systems (reasons to be discussed later on in this thesis). As shown in Table 1.1, one of the Dutch utilities estimates the contribution of the joint failure to the network failure as 31%. Cable systems are distributed components which means that in case of failure, the problem can be resolved by replacing a limited length of few meters of the failed cable around the failed cable joint as well as the failed joint. This results in a temporary interruption in power supply.
The occurrence of these interruptions and their duration
can be limited by proper maintenance actions.
1.2 Maintenance strategies As shown in Table 1.1, a relatively high number of failures in a cable system occurs due to external causes such as digging activity in the cable vicinity. However, still the majority of failures are related to deterioration by aging of the components. Despite being aged, a full scale replacement of the cable systems is not needed nor recommended from a technical and economical point of view. Asset managers are challenged with keeping the systems running while minimizing the costs involved i.e. they have to keep the balance between technical and economical aspects while providing continuous power for the customers (Figure 1.1). Proper maintenance actions on aged or on defective sections can prolong the life of the systems, thus preventing interruption of power supply. Figure 1.2 illustrates the conventional reliability bathtub curve performance. The life cycle of a population of assets is divided into three stages. Stage 1, or the so-called "Early life or Infant mortality", shows decreasing failure rate versus time.
This happens because weaker units within a population fail shortly after
being taken in operation. Next, the life cycle reaches the "Useful life or Normal stage", where the failure rate is on a constant low level and predictable based on the operation history. Finally, reaching the technical "End of life", results in an
4
CHAPTER 1.
Figure 1.1:
Figure 1.2:
INTRODUCTION
Aspects to be considered by asset managers.
Conventional reliability bathtub curve.
increased failure rate. Appropriate maintenance action should expand the asset life in this stage. There are generally three distinct categories of maintenance policies, namely corrective maintenance (CM), preventive maintenance (PM) and reliability centered maintenance (RCM) [1]. CM occurs after a failure, while the PM intends to avoid the failure. Basically, there exist two PM approaches, time-based maintenance (TBM) and conditionbased maintenance (CBM). The former one relies on a predetermined schedule
1.2.
MAINTENANCE STRATEGIES
Figure 1.3:
5
Condition based maintenance approach.
which results in reduced failure rates, however, on the expense of increased cost. The latter one, also called predictive maintenance, is performed when certain changes are being observed in system condition.
The RCM approach basically
focuses on properly combining all reliability factors, PM measures and involved expenses. In practice all the mentioned strategies are being exploited. CBM is a maintenance approach development which aims to sustain reliability in an economically justi ed way. From a technical point of view, it requires tools to provide appropriate insight into the condition of the system to assist asset managers in employing an optimal replacement and/or maintenance strategy. Various tools have been developed to monitor the condition of cable networks. Figure 1.3, shows a general overview and a very basic developed CBM strategy [6]. The rst step is to collect data through monitoring the condition of the cable system and historical database available for the components. Next step, addressed here as data handling, is to manipulate the acquired information to obtain meaningful abstract quantities representing the ongoing process in the insulation system.
The last
stage before the decision making process is to translate the obtained knowledge to the comprehensible measures required for decision making.
This is basically
done by predicting the probability of occurrence of an event based on obtained information through processing the collected data.
Further, this probability of
occurrence will be utilized to provide a risk index, presenting the severity of the existing condition. The risk index is used for further decision making process. There are several aging mechanisms leading to a failure in cable networks, e.g. thermal degradation, mechanical forces, electrical stresses and environmental condition. All aging phenomena result in changing insulation characteristics. These changes can be analyzed to identify the deterioration process and its stage. Several tools have been developed that are capable of monitoring speci c aging symptoms. These techniques are classi ed as electrical, thermal, mechanical or chemical according to the changes that they probe as measurable properties of the insulation. A second classi cation is related to whether the diagnostic technique is destructive or non-destructive. Table 1.2 brie y provides an overview of available diagnostic tools for MV cable networks.
Withstand test
AC breakdown DC test on the outer sheath Acoustic PD detection Space charge
Visual inspection Degree of polymerization X-ray radiography
7 8 9 10
11 12 13
Dielectric response: Return voltage, DC leakage current, TDR, Dielectric spectroscopy, tanδ, depolarization and polarization current Insulation resistance Thermal distribution Thermal resistance of the ground
Electrical PD detection
6
3 4 5
2
1
Measurement technique
indicates possible thermal aging misalignment and soft spots can be identi ed
measures space charge accumulation to identify water trees
measures the insulation resistance monitors the cable temperature measures the cable resistance versus the temperature
is used to identify water trees
is given in detail the text
Description
Electrical, in-situ, o-line With ber optics along the cable With ber optics along the cable (via a model) Non-destructive, electrical, in-situ, o-line (this method can be destructive) Destructive, electrical - laboratory Electrical, in-situ, o-line Non-destructive, electrical, in-situ, o-line Non-destructive, electrical laboratory laboratory, in-situ (for terminations) Chemical, laboratory Non-destructive, laboratory
Non-destructive, electrical, in-situ, o-line
Non-destructive, electrical, in-situ, o-line / on-line
Remarks
6 CHAPTER 1. INTRODUCTION
Table 1.2: Overview of the available diagnostic tools for MV cable networks
1.2.
MAINTENANCE STRATEGIES
7
One of the mechanisms associated with the degradation of the insulation integrity is partial discharge (PD) activity. PDs may arise as an indication of aging such as thermal aging or can be part of degradation process itself. In principle, PDs can be observed optically, acoustically and electrically. However, for an underground power cable, signals can only be detected at the cable end, leaving the detection of electromagnetic signals as the only feasible option.
These signals,
originating from the PD event, are capable to reach the cable ends where they can be recorded. Several tools have been developed to measure PDs. Table 1.3 presents the main characteristics of commonly used diagnostics tools developed in recent years to identify and locate PD sources in-situ.
Table 1.3:
Overview of common PD diagnostic tools developed for MV cable
systems - in-situ Testing method 50 / 60 Hz PD measurement
Remarks O-line Constant 50 / 60 Hz sinusoidal wave up to 3U0 for up to 60 minutes
Very low frequency
O-line
(VLF, 0.1 Hz) test system
Constant 0.1 Hz sinusoidal wave up to 3U0 for up to 60 minutes
Oscillating wave voltage
O-line
test system (OWTS)
Damping oscillating wave up to several kHz and up to 3U0
SCG (PD-OL) system
On-line elaborated in detail in this thesis
The o-line methods use an external energy source to energize the cable which is disconnected from the grid. In 50/60 Hz testing method [7], as mentioned in Table 1.3, a 50/60 Hz power supply is used to energize the cable under test. Basically, the size of the power supply needed, makes this technique unsuitable for on-site PD testing.
The other two o-line techniques utilize dierent power waveforms
which makes them suitable for on-site diagnostics. In VLF measurement technique, the cable under test is energized at very low frequency e.g.
0.1 Hz [8, 9, 10].
In oscillating wave voltage method, the test object is energized by a DC power supply up to service or higher voltage, and is then discharged through an air-core inductance connected via a solid state switch to a cable.
This results in slowly
decaying oscillating wave which is utilized to energize the cable sample [11, 12]. SCG system employs techniques to measure PDs on-line. This system is described in detail later on in this thesis.
8
CHAPTER 1.
INTRODUCTION
1.3 Research Objective Academic research on developing on-line partial discharge identi cation and location was initiated as cooperation between TU/e, KEMA, Dutch utilities and STW during the period 2001-2005.
The Smart Cable Guard (SCG) system, formerly
named as Partial Discharge On-line with Location (PD-OL), was developed and became commercially available in 2007. Research on further improving the system continued on two aspects. One research basically focused on the technical challenges of integrating the SCG in large scale in various cable networks [13]. The other, reported in this thesis, aimed to develop proper knowledge rules to assess the system condition. Years of experience with o-line measurement led to establishing mature knowledge rules for PD interpretation. These knowledge rules can not be fully exploited for on-line measurement since these rules depend to some extent on the characteristics of the diagnostic tool, such as stimulated PD activity through increased voltage level in o-line techniques or dependence on frequency and waveform of applied voltage. Data analysis for the SCG should take into account that data is continuously gathered, be it with intervals of about a minute where data is gathered over one power frequency cycle (see Chapter 3). Continuous diagnostics provide a vast data stream which must be interpreted in order to extract useful information on the insulation status. This research focuses on developing proper strategies and knowledge rules for on-line measurements which can be a decision support module added to the SCG units. These strategies are used to identify potential defective sites with PD activity, especially in noisy environments. Afterwards, the knowledge rules are employed to determine the condition of the insulation system at the identi ed defective spots through assessing the possible risk of failure and remaining life of the identi ed site.
1.4 Thesis outline The research presented in this thesis is carried out to establish an interpretation approach for partial discharge activities measured on-line for medium voltage (MV) cable insulation. To develop the interpretation layer, a key issue is to obtain a good understanding of the potential degradation mechanisms. Chapter 2 elaborates on possible defects that can exist in MV cable insulation, their degradation mechanisms as well as their potential root causes. Mostly, the degradation mechanisms in MV cable insulation are followed by partial discharge activity. Chapter 3 describes the partial discharge phenomena and introduces the available approaches to detect and measure this activity in the insulation. The Smart Cable Guard (SCG) system, as a measuring system, is explained in this chapter as well. In Chapter 4, pattern recognition methodology for PD interpretation is described. Possible identi ers and norms to present the insulation state in abstract form are discussed in this chapter. Generally, due to the enormous stream of data being collected through minute base measurements, a tool is required to automatically detect and identify the
1.4.
THESIS OUTLINE
insulation condition.
9
Chapter 5, introduces the algorithms and the approaches
employed to automate the interpretation process. Chapter 6 presents the application of the developed automated defect identi cation tool to the available partial discharge measurements results. In this chapter the capability of the tool to detect the partial discharges is illustrated. Chapter 7 summarizes the conclusion of this work and gives recommendations for future research.
Chapter 2
Defects and degradation mechanism in MV cable insulation systems Failure statistics presented in Table 1.1, showed that the majority of the breakdowns in MV grids occurs due to the failure of components within cable systems, especially the cable joints.
There are various types of cables, cable joints and
cable terminations utilized in power cable systems, each of which may suer from various insulation defects and degradation mechanisms. The cable connection is a distributed system. Whenever a part in a cable connection shows strong degrading symptoms, it can be removed and replaced. Such repairs may not only result in an exchange with a similar component, but it also could involve replacement by another type of the same component or even a modi cation of the topology of the circuit. If, for instance, a large section of the PILC cable is degraded, that part can be replaced by a section of XLPE cable, which is connected via joints that are dierent from the joints incorporated in the circuit before. Degradation mechanisms in insulation may vary from component to component.
One mechanism
can be of no importance for one type, while it could be hazardous for another type of the same component. Good understanding of the degradation mechanism helps in selecting the right characteristics to be investigated as an indicator for potential defects in the insulation media. In a further stage, this knowledge may assist asset managers to decide more eectively on the measures to be taken to guarantee uninterrupted power delivery. A power cable network comprises several components namely the cable itself, the joint which connects parts of cables and the termination at the end of a cable connection. Each of these components may have dierent insulating media. Cables can be either paper-oil insulated or made of extruded synthetic insulation. Joints and terminations can be mastic, resin, paper, oil- lled, pre-moulded and hot/cold shrink. Various degradation processes have been recognized for each component type, but at the end they may all lead to a single identi er which can be used to detect the existence of a defect in the insulation. As it will be shown in this chapter, most of these defects results in PD initiation during the aging mechanism, which makes this measure as a quali ed identi er to assess the insulation condition. 11
CHAPTER 2.
DEFECTS AND DEGRADATION MECHANISM IN MV
12
CABLE INSULATION SYSTEMS
In the following section the main defects known for MV cable network insulation are presented. Next, the degradation mechanisms stimulated by these defects are discussed separately for the various components in the cable network.
The last
section is devoted to study the possible root causes that result in the failure within cable insulation systems.
2.1 Defects in MV cable insulation system Insulation breakdowns are caused by degradation processes that are initiated by defects in the insulating media resulting from:
Bad design with an inadequate design testing Wrong production and inadequate production testing Mistakes during transportation and installation Extreme operational conditions Aggressive environmental conditions Excavation damages Normal operational conditions over a long period of time
Several defects have been found for MV cable insulation through visual inspections of the failed components. Schematics 2.1 to 2.3 present a quick overview of common defects classi ed for PILC and XLPE MV cables and accessories.
Both for the
cable as for its accessories, the main distinction is the type of insulating material, either PILC (paper-oil insulated) or XLPE (extruded synthetic insulation). These schemes are very much simpli ed to avoid extensive elaboration on different aging levels and contributing factors. For instance, schematic 2.1 presents an overview of common defects in paper insulated cables.
Outer damages to lead sheath Corrosion of lead sheath Dry out of paper layer Depolymerisation of the paper layer Embrittled insulation
The defects result in breakdown through either wet degradation mechanism which occurs due to moisture ingress from vicinity soil, or through thermal aging referred also as dry degradation which develops by temperature rise and heating up of the cable. Details of the mechanisms are discussed in the next section. Similar approach can be developed as illustrated in schemes 2.2 and 2.3 for XLPE cable degradation and for degradation of the accessories.
2.1.
DEFECTS IN MV CABLE INSULATION SYSTEM
13
Figure 2.1: Quick overview of defects and the degradation mechanisms classi ed for PILC MV cable
CHAPTER 2. 14
DEFECTS AND DEGRADATION MECHANISM IN MV CABLE INSULATION SYSTEMS
Figure 2.2: Quick overview of defects and the degradation mechanisms classi ed for XLPE MV cable
2.1.
DEFECTS IN MV CABLE INSULATION SYSTEM
15
Figure 2.3: Quick overview of defects and the degradation mechanisms classi ed for dierent joints used in MV cable system
CHAPTER 2.
DEFECTS AND DEGRADATION MECHANISM IN MV
16
CABLE INSULATION SYSTEMS The next section summarizes the main degradation processes occurring in cable
network components.
2.2 Degradation mechanism Schematics 2.1 and 2.2 list the main aging mechanisms for the cable sub-components. The discussion in this section will be focused on degradation of the insulating material itself.
Perforation of the lead sheath for PILC cable will not harm the
insulation directly, but can lead indirectly to failing insulation performance by e.g. promoting moisture ingress. For XLPE cable, wear of the outer plastic sheath is not considered but focus is again on the main insulation layer. The cable joint types listed in schematic 2.3, either applied for PILC or XLPE cable, each have their speci c failure mechanisms. These mechanisms and their associated causes are discussed in the following subsections.
Paper insulated cables Thermal aging is the main aging mechanism in paper/ uid- lled insulated cable systems [3, 14, 15]. Paper and the compound deteriorate over time and thereby their electrical and mechanical properties are impaired.
The cellulose chains in
paper tend to break down due to aging, and as a result the mechanical strength of the paper decreases over time. Decrease in mechanical strength of the paper makes the paper brittle [3]. Meanwhile, aging of the compound results in formation of a cheese-like wax over the paper layer and in the butt-gaps (Figure 2.4a). This aging type will not result in an immediate breakdown of the insulation under normal operating condition. But the rate of deterioration of the paper and the compound is greatly accelerated by increased temperature, and by the presence of moisture or oxygen [16]. Besides the possible thermal aging, this mechanism may eventually result in an initiation of intensi ed PD activity in the open spaces which also leads to breakdown.
Figure 2.4:
Defects in paper insulated cables: a) Cheese-like wax formed on the paper
insulation layer in paper insulated cable; b) Corroded lead sheath in PILC cable; c) Moisture ingress in PILC cable
2.2.
DEGRADATION MECHANISM
17
Inadequate sealing or poor water tightness at the end of the cable section, a past failure or damages to the lead sheath (Figure 2.4b) provides a path for moisture to penetrate the paper layer in the insulation. Moisture (in liquid form or vaporized state) distributes over the paper and reaches deeper layers while crossing the gap between the wrapped layers. Moisture ingress (Figure 2.4c) changes the properties of the insulation material. In case that the insulation already suers from thermal aging, the moisture presence may even intensify this condition by reducing the dielectric strength thus increasing the dielectric loss by an order of magnitude [14] which promotes thermal aging even further. During high loading conditions the lead sheath expands due to the pressure built up by the expansion of the inner insulating compound. However, after cooling down, the compound and the lead sheath will not fully return back to their original state, which leaves a gap between the layers, thus providing a site for PD activity to occur. Meanwhile if the moisture ingress is already degrading the material, the progress will be intensi ed in presence of a gap between the lead sheath and the compound.
Moisture is absorbed by the paper insulation and results in further
PD activity. Mentioned defects, separately or combined, can cause enhanced PD activity which erodes the paper layers and causes further degradation resulting in the formation of electrical trees and consequently ultimate failure of the insulation. Figure 2.5 illustrates the formation of electrical trees in a paper tape. Electrical trees are channels in the form of tree-like patterns that originate from a defect site, e.g. gas cavities, conducting inclusions, intrusions [17], eroded surface in a void [14]. These channels can continuously develop and further degrade the insulation. The electrical tree will result in a breakdown when it bridges the insulation.
Extruded cables In practice, formation of water trees is the most dominant aging mechanism in polymeric insulated cables. The trees form a diuse structure in a dielectric insulating material with a bush or fan resemblance [18]. They are referred as vented
Figure 2.5: Electrical treeing on paper layer of the insulating material
CHAPTER 2.
DEFECTS AND DEGRADATION MECHANISM IN MV
18
CABLE INSULATION SYSTEMS
Figure 2.6: Water treeing in extruded cable: a) Vented tree, b) Bow-tie tree
trees (Figure 2.6a) if they are initiated from within the insulation and bow-tie trees (Figure 2.6b) if they originate from the insulation-conductor interface. Water treeing is caused by moisture penetration into the insulation in the presence of an electric eld and clustered impurities.
The rst generations of
the polymeric insulated cables were not water tight. Moreover, they had lots of clustered impurities both in the semi-conducting screens and in the insulation. As a result, the moisture within the soil in the vicinity easily permeated into the semiconducting screens and the insulation and promoted the growth of water trees, also on the inner side of the insulation by crossing the polymer.
In fact, water
treeing is a corrosion of the polymer with its main direction of growth in line with the electric eld. Water content in the corroded area changes the electrical properties of the insulation.
It, generally, reduces the electric breakdown stress
level of the insulating material. However, the cable is still capable of withstanding the operating condition even if the tree fully bridges the inner and outer conductor through the insulation. If the moisture content of the soil in the vicinity decreases or it is dried, then the degraded region with in the cable insulation will dry out during the on load period due to the heating by the losses from the load current [19]. The breakdown stress of the water treed insulation can be restored up to 50% [18] which itself reduces the likelihood of breakdown. However, if the soil is humid, the cable insulation will continue absorbing moisture at the defective sites which exposes the cable insulation to further degradation. Besides, during the o-load period, an increase in the ambient humidity results in moisture absorption at the defective sites, which exposes the insulation to higher probability of breakdown. Due to the water presence and impurities in the insulation the electrical eld is disturbed.
Mostly, due to this presence of water and impurities dissolved, the
quality of the insulating material gets poor, so it can not withstand the electrical eld enhancement inside the water tree and therefore, an electrical tree is initiated from inside of the water tree.
In some cases, electrical tree growth starts from
2.2.
DEGRADATION MECHANISM
19
the tip of the water tree channels due to the fact that the electric eld strength is lowered inside the tree channels and is enhanced in the neighboring media [20]. Generally, conversion of water treeing to electrical treeing at the last life stage of the insulation results in cable insulation failure in a relatively short time [21]; i.e. it may occur in a range of minutes under a laboratory conditions and may take longer time under real operational condition with lower electric eld.
Resin joints The resin used in cable joints can either consist of hygroscopic or non-hygroscopic material. Hygroscopic resin tends to absorb moisture present in its vicinity. Therefore, such material should be contained in a carefully sealed joint cast.
Non-
hygroscopic resin can behave similarly if the resin is mixed poorly. Thus, it should be kept away from wet environment as well.
Due to inadequate sealing, which
may have been resulted from either poor installation practice or mechanical forces from conductor expansion during high cyclic load, moisture penetrates into the joint during operation. Moisture will partly be absorbed by resin molecules which results in changes in chemical, mechanical and electrical properties of the resin due to altering the molecular structure. Part of it will remain on the surface of the compound providing a site for discharge activity at the surface of the material. The disturbed electrical eld with an enhancement in the vicinity of the conductors may result in failure of the insulation medium. The deterioration of the resin compound could be further intensi ed due to possible misplacement of the conductors. If the conductors are not centrally assembled (Figure 2.7a), they get bended, which results in changes in the electric eld distribution. Increase of electrical eld stresses around the conductors gives rise to the PD activity which further leads to formation of electrical trees that in time evolves into a breakdown. However, not always moisture in insulation is responsible for deterioration of the resin.
Cavities created during a poor mixing also result in enhancement of the
electrical eld in the insulation with an increased level at the cavity site.
This
may cause PDs that further erode the material through electrical treeing which may lead to breakdown (Figure 2.7b).
Figure 2.7: Failed resin joint caused by: a) misplaced conductors, b) poor mixing
CHAPTER 2.
DEFECTS AND DEGRADATION MECHANISM IN MV
20
CABLE INSULATION SYSTEMS
Figure 2.8: Mastic joint failed in service as a result of conductor extension [22]
Mastic joints Mastic joints mostly suer from conductors being bent inside the joint. The high current load, especially high current cycling loads, could cause increased mechanical forces due to the extension of the conductors [22]. The deformed conductors might contact the metal casing, especially if no insulating caps are used, and result in a failure. Figure 2.8 shows an example of a mastic joint failed in service due to the conductor extension. Besides, mastic material can absorb moisture that enters the joint cast as a result of poor water tightness caused by bad installation or mechanical forces from conductor expansion during heavy load periods. The moisture is speci cally soaked up in the cavities that may exist in the mastic compound. The moisture absorption not only causes chemical changes in the material but also initiates PDs at the cavity site followed by degradation of the entire material. However, such degradation can be initiated or intensi ed due to deformation of the conductors, which results in inhomogeneous electric eld stresses that cause deterioration of the material. Basically, such degradation is intensi ed when the system is exposed to an overloading condition.
Oil- lled joints As in the previous cases, moisture ingress (Figure 2.9a) is a common mechanism to degrade the oil insulation. This generally happens due to poor sealing of the joint cast. Presence of the moisture in oil reduces the dielectric strength of the oil and weakens the insulation. Besides, the high current load, especially high current cycling loads, could cause increased mechanical forces due to the extension of the conductors [22]. Therefore, the conductors could get deformed inside the joint cast. If the conductors are bare inside the iron cast, then such deformation will result in an immediate breakdown. In case that the conductors are individually insulated, then the increased mechanical forces will create bending and deformation along the conductors, causing further mechanical degradation to the weakened insulation and creating cracks inside the insulation. Moreover, oil may leak from the joint either into the insulation of the cable or to the soil during the load cycles. This reduces the oil level (Figure 2.9b) in the joint, which causes spark production at the surface of the oil. Such discharges create oating carbonized oil substances,
2.3.
ROOT CAUSES
Figure 2.9: a) Moisture ingress into oil- lled joint,
21
b) Low oil level in oil- lled joint
which move over the surface and after a while may cover the whole surface. The carbonized path created between the connector and the earth screen results in formation of the short circuit path and eventually failure.
Shrink/pre-moulded joints Degradation processes in shrink and pre-moulded joints can be initiated if air lled cavities exist in or at the surface of the material due to a poor shrinking process.
Due to electric eld enhancement at the edges of these cavities, PD
activity is started. PDs cause erosion, further degrading the material. Moreover, there are other aws such as knife cuts in the insulation created during the joint installation, presence of pollution in the insulation and gaps between cable-ends connected inside the joint, that can provide preliminary sites for degradation to start.
2.3 Root causes Good understanding of the deterioration process helps in preventing premature failures in the cable system. Knowing the possible causes for defects, and further on the degradation, is also valuable information that can be used to optimize the component lifetime. Several factors are involved in actuating the degradation phenomena.
Generally, these factors can be classi ed as defects induced in the
insulation during manufacturing, handling, and in service periods.
Manufacturing defects Manufacturing defects address the root causes that result in delivering defective cables and accessories due to design and engineering de ciencies. Generally, the type test is intended to prevent such problematic components, however, it can happen that the type test is not su cient to evaluate the quality of the joint. Poor treatment of the insulating material at the manufacturing stage may result
CHAPTER 2.
DEFECTS AND DEGRADATION MECHANISM IN MV
22
CABLE INSULATION SYSTEMS
in cavities and other defects in the insulation. Mainly, such defects are identi ed during the production testing for examining the quality and factory acceptance test (FAT). Mostly, cables are delivered without defects with (in the past mainly) an exception of insulation material susceptible for the creation of water trees. Such water trees can appear in large scale along the insulation in a later stages. The water treeing may be initiated from small impurities hidden in the semiconducting screens or the main insulation.
Handling defects Handling defects category includes all the defects imposed during the delivery and laying of cables and accessories.
Incompetent handling during the transporta-
tion of the cable and the accessories damages the insulation or the protection layers. etc., providing initial sites for degradation process to begin. Apart from transportation damages, the major fraction of damages to the insulation in this category are caused by lack of expertise of the personnel involved in assembling the cable system or lack of su cient assembling guidelines provided by the manufacturer. Insu cient assembling knowledge can result in several problems such as misplacement of the conductors in a joint, too fast preparation of the joint which creates all types of defects, rough treatment of the joint which leaves knife cuts on the joint, and so on. These defects provide an initiatory factor for degradation of the insulation. However, one should bear in mind, this may not only happen due to lack of expertise, but also, pressure and stress involved for fast utilization of the system, bad weather conditions and other similar factors have impact on the probability of creating defects during the handling and installation stage. The site acceptance test (SAT) are developed to test the quality of the installation, however, these tests are not always capable of revealing the potential created defects due to the fact that either they are applied with the wrong test parameters (for instance DC instead of AC) or they are simply not su ciently developed and/or designed to identify certain defect under certain circumstances.
In-service defects This category covers the damages created in the insulation media after the system is put into operation. This group includes damages caused by either operational condition of the system, environmental condition of the system vicinity or those created by third parties.
Operational condition - Many defects can be created within the insulation because of its operational stresses (see [22]). Due to load cycling, the insulation encounters thermal and mechanical stresses which damage the insulation. Over time, as a result of chemical transformation within the insulation structure, insulation loses its dielectric strength and consequently may encounter breakdown. For instance in case of paper insulated cable, such structural transformation results in drying out of paper, decreasing its mechanical
2.3.
ROOT CAUSES
23
strength, and leading to failure of the insulation. Temporary overvoltages can create insulation degradation on a small scale that in a later stage of life
might give problems even under normal operation conditions. Environmental condition - After laying the cable in soil, the environmental condition plays an important role in extending or shortening the lifetime of the insulation. Depending on the ambient condition of the soil the defects may be initiated, may develop further, or even can disappear. Variation in humidity content of the soil is one of the factors that have impact on the aging of the insulating material. A drop in soil water level increases the soil temperature which in combination with a raised temperature due to the load cycling in uences the operational conditions (see: [22]). It exposes the cable system to thermal and mechanical stresses which may induce defects in the insulation.
Unstable soil makes the cables to sink especially in the peat,
which results in mechanical stresses on the accessories.
Such forces result
in movement and misplacement of the conductors in the joint cast which induces mechanical degradation. Besides, the mechanical forces to the cable will have impact on the water tightness of the joint, which creates a path for moisture penetration into the insulation. Moreover, the lead sheath can corrode under certain circumstances which causes moisture ingress to the insulation as well. Flatness of the ground where the cable is buried in is also playing an important role in the development of defects in the insulation. If the cable is laid in hilly regions, then the PILC cable parts on the slopes might dry out (depending on the cable design). This is also the case when the cables are entering substations and go up to the terminations. In this case also the PILC cable and/or its terminations are exposed to possible
paper dry out. Third party forces - Apart from failures caused by operational and environmental conditions, third party's active or passive involvements can harm the insulation in dierent ways. Digging activities taking place in the vicinity of the cable may either directly damage the cable insulation or indirectly via vibrations created in the soil. Heavy tra c on the road also introduces vibrations in the soil. Such vibrations along the soil below which the cable is laid may cause displacement of the conductors as well as result in reduced water tightness of the joint. DC current from e.g. railways owing trough the earth may partly follow the cable outer metal sheath of the screen and corrode the metal sheath especially where the current exits the sheath. This will result in water ingress and consequently may lead to premature failure of the insulation.
Chapter 3
Partial discharge monitoring Aging mechanisms leading to failure in cable networks can be classi ed based on their underlying physical interaction in thermal degradation, mechanical forces, electrical stresses and environmental conditions.
All aging phenomena have in
common that they result in a change in the properties of the insulation.
Dif-
ferent aging mechanisms can act simultaneously, as part of the overall insulation deterioration process.
That means that there can be essentially dierent quan-
tities that could be monitored to identify the degradation process and its stage of development. Yet, the best choice is the one which does reveal the dominant degrading phenomena, and at the same time is practical feasible to be employed on an economically realistic scale.
In fact, this choice is of fundamental impor-
tance anywhere in the eld of diagnostics. One of the common mechanisms related to failure of cable insulation media is partial discharge (PD) activity. Indeed, the fastest aging mechanisms in cables are associated with PDs [23]. Hence, diagnostic systems capable of monitoring this activity have attained signi cant popularity as a condition assessment tool, especially for high voltage (HV) and medium voltage (MV) cable systems [24, 25, 26, 27, 28, 29, 30, 31, 32, 33]. In the following sections the importance of PDs in cable insulation degradation is discussed. Next, the pros and cons of on-line versus o-line cable diagnostics will be highlighted. The focus will be on a speci c technique called SCG (Smart Cable Guard) developed for on-line condition monitoring of the MV power cables and employed during this research work as a diagnostic tool. This technique which has many advantages including the possibilities to identify defects in an eective way is further introduced in the last section.
3.1 PD activity and aging PDs are small localized discharges in insulating media, which can occur due to enhanced electric eld in the insulation caused by discontinuities in the insulation. They can appear from defects within the insulation media as a result of poor design or fabrication, bad installation, heavy service conditions (too high voltages, 25
26
CHAPTER 3.
PARTIAL DISCHARGE MONITORING
too high currents), long term in-service time under normal operational conditions or third parties involvement during the operation time. PDs are not a full breakdown, but only bridge a part of the insulation. They serve as an indicator for aging insulation, and they can also be part of the degradation process by eroding the insulation thus shortening its life time and even evolve into a complete breakdown. Defects resulting in PDs come in many forms, but have in common that once their size and the voltage over the defect, reach critical limits, the occurrence of rst free electron can trigger a PD. This is usually a fast event, initiating a relatively low amplitude high frequency transient current. This current extinguishes usually on a nano-second scale. In a cable a transient signal starts to propagate from the defect. The charge displacement by the PD results in an electric eld opposing the external eld in the defect. Whether it repeats itself depends on the applied voltage, the defect type and its stage of development. For AC voltages the external eld will change and a new PD can be initiated as soon as the voltage over the defect crosses the PD inception level again. Changes in material dimension during shrinkage or expansion of a void in the insulation on load cycling may ignite the PDs as well. For constant applied voltage (DC) charge leakage can result in reestablishing an electric eld causing repeated PDs. For instance, pulse numbers
2
measured for cavities embedded in 15 kV class, 380mm , XLPE cable is reported. For a cavity of 0.2 mm, seven PD/day, a hundred PD/day in a 0.5 mm cavity and about a thousand PD/day in a 1 mm cavity can be monitored at nominal voltage [34]. Repetitive PD activities can result in a formation of conductive channels, a process called electrical treeing, in the dielectric material. Generally it is believed, that the growth of an electrical tree leads to breakdown as soon as the tree fully bridges the insulation medium. However, in reality, this may not be an immediate event. After the electrical tree bridges the insulation, thermal heating of the existing tree branches in combination with further growth of the electrical tree can cause further deterioration of the material and lead to the ultimate breakdown. Moreover, repetitive discharge activity does not only electrically harm the insulation but also causes a lower mechanical strength as well as may cause chemical deterioration. Changes in the dielectric properties, e.g. in the form of increased conductivity can intensify the local electrical stress at the tree tips accelerating the degradation process. The severity of PDs strongly depends on the type of insulating material. Especially, the distinction between paper-oil and synthetic cable insulation should be noted.
In paper-insulated MV cables, the actual current load with its resulting heat and repetitive discharges cause permanent chemical changes within the affected paper layers and impregnating dielectric uid (Figure 3.1). Over time, partially conducting carbonized trees are formed. This places greater stress on the remaining insulation, leading to further growth of the damaged region, resistive heating along the tree, and further tracking. This eventually results in the complete dielectric failure of the cable and, typically, an explosion may occur. PDs generally dissipate energy in the form of heat which
3.1.
PD ACTIVITY AND AGING
27
Figure 3.1: Chemical changes in paper layers and impregnating uid - MV PILC cable
may cause thermal degradation of the insulation, although the level is normally low. Basically, paper-insulated cables can resist PD activity for a long
time, even up to several years. Generally, organic and extruded polymers are more sensitive to PD activity. Extruded insulation cannot resist even a low PD activity, let alone signi cant repetitive PDs. Preliminary PDs expand in a weak spot very fast, depending on the electric eld stress. The defect evolves from a small void or cavity into an electrical tree which grows along the insulation medium in a matter of seconds, hours or weeks and ultimately causes a full breakdown.
It should be noted that a majority of faults occurs in cable joints, which connect dierent cable sections. These joints come in many types (Chapter 2), each with a speci c sensitivity to PD activity. Generally, partial discharges are classi ed in three main groups, namely internal, surface and corona discharges. Internal discharges occur in a cavity within the insulation media which develops further to electrical trees as a result of cumulative PD activity. Figure 3.2 [23] shows the development of an internal defect to a tree which ends up in a full breakdown. Surface discharge occurs along the dielectric interface.
The formation of a
conductive path along the surface of the insulation results in tracking which further converts to electrical treeing and eventually complete breakdown. The electric eld strength in the dielectrics where discharges occur at the surface is fairly low. Figure 3.3 shows the formation of tracking from a surface discharge. Corona discharges mostly happen at sharp edges in a gaseous or liquid media due to the presence of an inhomogeneous eld. Such discharges are considered to be less harmful to the insulation as compared to the internal and surface discharges depending on the material.
28
CHAPTER 3.
PARTIAL DISCHARGE MONITORING
Figure 3.2: Growth of internal defect from a cavity to electrical treeing (copied from [23])
Figure 3.3:
treeing.
Formation of tracking from surface discharge followed by inward electrical
In cable systems, the classi cation of the discharges leads to the following events:
Corona in air or oil Surface discharges between liquid and solid, liquid and gas, solid and gas, solid and solid (Figure 3.4 shows an example of surface discharges in the air lled area between 2 interfaces in cable) Internal discharge in cavities and from treeing
Each discharge activity results in a speci c PD magnitude distribution and it leaves a speci c pattern with regard to voltage which will be further discussed in Chapter 4.
3.2.
DISCHARGE MAGNITUDE
29
Figure 3.4: Examples of surface discharge between two solid interfaces
3.2 Discharge magnitude The behavior of internal and surface discharges can be simpli ed by means of the abc model shown in Figures 3.5a and 3.5b, respectively [35]. In this model the discharge is interpreted as a sudden short circuit of capacitor c, which stands e.g. for a small cavity inside the dielectric material (Fig. 3.5a) or a charge over a part of the surface (Fig.
3.5b).
The actual charge displacement that occurs due to
a PD event is not directly measurable.
Therefore, the apparent charge is used
instead. This is the charge arising at the terminations, which can be accessed for PD detection. The apparent charge of a PD pulse is that charge which, if injected within a very short time between the terminations of the test object in a speci ed test circuit, would give the same reading on the measuring instrument as the PD current pulse itself.
The apparent charge, usually expressed in picocoulombs is
only a fraction of the charge locally involved at the defect site. In most cases the ratio between the apparent charge and the real charge is unknown, which adversely in uences the interpretation of the PD level and the severity of the defect.
Figure 3.5:
"abc" model representing a) internal discharge, b) surface discharge
30
CHAPTER 3.
PARTIAL DISCHARGE MONITORING
Discharge events can cause a deterioration of the material by the in uence of electrons or ions causing chemical transformation of many types. The detection and measurement of discharges is based on the exchange of the energy taking place during discharge event. This exchange manifests as electrical current pulses, dielectric losses, electro magnetic radiation, acoustic wave, increased gas pressure, chemical reaction, etc. Therefore, a measuring technique based on the observation of any of the above phenomena can be used for attaining the insulation state. Electrical PD detection methods are based on the appearance of a PD current or voltage pulse at terminations of a test object which can be a sample test specimen or a large apparatus. For distributed components, as underground power cables, the signals have to travel up to several kilometers distance to the cable terminations before they can be detected. Detection of electric signals is then the only feasible method, since all other expressions of energy associated with the PD will be quenched. The waveform, going to either side of the cable at the PD origin can be presented [36] by a Dirac pulse:
iP D (t , 0) =
1 Qapp δ (0) 2
This PD signal propagates through the cable over distance
(3.1)
z ; for one direction:
IP D (ω , z) = IP D (ω , 0) e−γ(ω)z
(3.2)
1 2 Qapp and γ (ω) is the propagation coe cient which includes both signal attenuation and propagation velocity. The PD signal will transmit to
where
IP D (ω , 0) =
the RMUs where it can be detected. The current transmission coe cient from the cable to the RMU is given by [36]:
T (ω) = where
ZC
2 ZC ZC + Zload
is characteristic impedance of the cable under test and
(3.3)
Zload the impedance
of RMU being approximated as a lumped component. The PD signal transferred to RMUs in case of the SCG system is detected by inductive sensors. The sensor is placed around the cable, but past the last connection of the cable screen to ground, or around the last earth connection [25]. The sensor output after ltering will be:
V (ω) = Zsens · f (ω) · T (ω) · IP D (ω , z) Zsens
(3.4)
is the (ideal) constant transfer in terms of current sensor output voltage per
enclosed detected current. The function
f (ω)
takes the bandwidth limitation of
the sensor and connected equipment into account. Finally, the charge contained by the sensor is:
3.3.
PD DIAGNOSTICS: ON-LINE VERSUS OFF-LINE
Qsens =
1 Zsens
31
F T −1 (V (ω)) dt
(3.5)
=
F T −1 (f (ω) · T (ω) · IP D (ω , z)) dt
Propagation characteristics of power cables allow detection even after propagating over ve or more kilometers, however with reduced sensitivity and bandwidth. PD diagnostic tools, which are sensitive to small localized electrical defects both inside and outside the insulation media, are widely used for detection, identi cation and location of the defects inside the insulation medium.
Diagnosis of the
insulation can be done either on-line or o-line. The following section compares both approaches.
3.3 PD diagnostics: on-line versus o-line PD diagnostics can be carried out both o-line and on-line. O-line means that the component is taken out of service in order to perform the PD analysis. Test parameters can be adjusted to optimize the outcome of the diagnostic technique. For on-line tests the component remains connected to the grid. The operational parameters can hardly be varied, but detected PD activity is characteristic for operational conditions.
Depending on the PD activity inception and extinction
voltages in defective sites, any of the techniques either on-line or o-line can be used. Generally, various defect types can occur in the insulation. Each of them gives rise to dierent PD activity characteristics. According to [34], PDs detected at the system nominal voltage can be classi ed in three groups considering the inception and/or extinction voltages:
Group I includes PDs which require inception and extinction voltages for discharge ignition below operating voltage. PDs in this group can be detected by both on-line and o-line methods. Group II addresses the PDs for which inception voltage is above the nominal voltage, but the extinction voltage is below the nominal voltage. Such defect is basically triggered during abnormal conditions, namely disturbances in online diagnostics or when testing o-line with voltage larger than
U0 .
Once
the activity is triggered it self-sustains and continues till the system is shut down. Discharges classi ed in this group are also captured with both on-line
and o-line techniques. Group III consists of PDs in which the inception and extinction voltages required for PD initiation are above system operating voltage. In this case PDs are generated during overvoltage condition and disappear once the condition is resolved. Defects in this group can only be detected by o-line approaches (assuming the on-line approach is capturing PDs with a low percentage of the time as is normally the case so far, this might change in case on-line
32
CHAPTER 3.
PARTIAL DISCHARGE MONITORING
monitoring tools become available that watch the cable on a 100 % time basis or close to that). Since each of the approaches has its own advantages and disadvantages, a choice which diagnostic approach is more feasible, both economically and technically, should be made.
PD activity is in uenced by the size of defect, type of defect
and the applied voltage level. Smaller defects that cannot be detected by on-line measurement, can be captured more easily by o-line techniques, since the voltage can be adjusted up to the level at which PDs become detectable.
O-line
measurement can also be a proper tool for quality control of the insulation during the manufacturing, again due to the possibilities of performing measurement with voltage raised up to 1.5U0 or more.
However, energizing the cable higher than
the nominal voltage level can proof to be harmful to the insulation. In that case, the test becomes part of the degradation process. On the other hand, the voltage can be regulated to a level enabling the observation of upcoming defects. The disconnection of the component under test generally results in lower noise levels and therefore better detection sensitivity. There is more freedom in the choice of PD sensors to be applied, because they can be installed when the component is not energized. Since one cable end is left open, clear PD signal re ection can be expected allowing one sided PD detection and location based on Time-Domain Re ectometry (TDR, [37]). In o-line measurement, calibration of both pulse propagation speed and PD magnitude is relatively straightforward by signal injection when the cable is isolated from the grid [37]. On-line measurement is more sensitive to noise and disturbances since the cable connection under test is connected to the rest of the grid. Therefore, distinguishing between noise and PDs from a defect can be a challenge in on-line diagnostics. Also installation on life circuits is a complicating factor and the eect by the connected grid complicates interpreting signals aecting both determination of time-of arrival estimation (location accuracy) and the PD magnitude (calibration). Nevertheless, the advantages of on-line measurement justify eorts to overcome the technical complications mentioned above:
On-line measurement monitors the cable system under real operating conditions. Temperature and humidity can aect the activity. Load cycles during the day/night result in temperature variations that correlate with PD activ-
ity. While o-line measurement requires switching-o the connected load, which consequently results in cooling down the cable that in uences the PD behavior, on-line measurement monitors the cable under exact normal operating
condition, including the daily/weekly load cycles. Since the power supply does not need to be switched o, on-line measurement can be done at any time.
In an o-line case, rerouting power delivery to
isolate the cable circuit during the measurement is a delicate procedure which can take even more time than the actual diagnostic test.
3.4.
PD DIAGNOSTIC TOOL - SMART CABLE GUARD
33
Conventional PD detection techniques can only identify discharges in relatively short cable lengths between two substations that can be isolated from the grid without interrupting power to customers. O-line method is, per de nition, restricted to one cable section, more cables can't be taken out of service without having a serious outage. However, this is not a restriction at all in on-line measurement. The superiority of on-line to o-line measurement is related to the possibility of trend watching. PD activity changes over time and these changes yield valuable information on aging progression. O-line measurements have limited capability of indicating the insulation state over time. They only provide snapshots of the insulation status during the cooling down of the cable and once every certain period of time e.g. once or a few times a year, while on-line measurements are capable to monitor the insulation condition continuously to detect upcoming defects like water ingress, mechanical damages, etc., and
monitor them over time. On-line techniques are capable of capturing the PD activities that only appear for a short time, which also make them superior to o-line techniques
In conclusion, on-line PD measurement is non invasive and non-destructive predictive test procedure, giving a representative picture of PD activity under normal load conditions.
It allows observing actual trends in PD behavior which may
indicate the actual insulation condition of the cable.
3.4 PD diagnostic tool - Smart cable guard Research invested during the last decades resulted in developing various diagnostic tools for medium-voltage power cables to be used either o-line or on-line. These PD measuring tools are capable of detecting a defect in the insulation, but not all of them are suitable to identify and locate at the same time [37]. O-line techniques which are designed for defect identi cation and location include one sensor which performs the measurement of both the PD signal itself and its re ection at the far end.
Such method may be hampered by possibly weak re ection if it would be
applied for on-line situations, due to the load connected to the cable being close to the cable characteristic impedance.
On-line techniques can employ a similar
approach based on measuring with two sensors, each at one cable end (Figure 3.6). The SCG (smart cable guard), was developed for PD monitoring in MV cable systems. This system comprises two units, to be placed at both ends of a cable connection. Each sensor is connected to a supporting computer that can communicate via Internet with the data control center. Each unit consists of an inductive sensor/injector unit (SIU) and a controller unit (CU). Inductive detection is chosen to allow for safe installation, avoiding galvanic contact with energized parts.
The inductive sensors employed in SCG include a
34
CHAPTER 3.
PARTIAL DISCHARGE MONITORING
coil with magnetic core which can be clamped around the cable end. Figure 3.7 schematically shows typical components including transformer, and cables, in MV substations or RMUs. The preferred locations to install the sensor are indicated with circles. Installing the sensor at these locations results in measuring only signals from the cable, while providing safe installation. Each of the shown locations has its own pros and cons which are further discussed in [25, 37]). Detection and location of PDs by the SCG system involves measurement, synchronization and calibration.
Measurement: The measurement is done through the sensors clamped around the cable ends with regular intervals. Each measured record is sampled with 50 MHz, su cient to cover relevant frequencies contained by PD signals which have
Figure 3.6:
Figure 3.7:
coil
[25, 37]
PD measuring system designated for on-line detection and location
Typical RMU layout with indicated potential positions to install inductive
3.4.
PD DIAGNOSTIC TOOL - SMART CABLE GUARD
35
traveled hundreds of meters or more (up to 5 MHz). A record contains one full power frequency cycle, i.e. 20 ms. In each record an injected reference pulse is included. At each cable end the record is analyzed. Arrival times and magnitudes of the signals which are identi ed as PD events are extracted. The parameters are communicated to a control center where the information is combined to nd the PD locations.
This whole process takes about one minute after which the next
record can be taken. The data collected continuously are stored in a database.
Synchronization: The PD location is extracted from the dierence in time of arrival at both cable ends. Therefore, both units need to be synchronized. The synchronization is accomplished by pulses injected through inductive coils (a patented solution) in regular basis at one cable end and measured at both cable ends [37]. The total cable propagation time is obtained and synchronization of both units is accomplished by these reference pulses [37].
Calibration: It is also required to calibrate the measuring system. To this end, the injected signals for synchronization can be used as well.
In principle, the
transfer of the injected waveform to the far end is measured and a model can be constructed for the frequency response. Such a model includes parameters for the signal propagation along the cable and signal coupling at the cable ends. From this model the transfer can be estimated of a PD arising anywhere in the cable.
Data interpretation: Proper interpretation is needed to actually give a meaning to PD measured along the cable system. The data can be correlated to a potential type of the defect. They also can be used to estimate the status of the defect as well as the potential risk and failure time. The next chapter describes the pattern recognition techniques employed to interpret the PD data.
Chapter 4
Partial discharge pattern recognition Condition-based maintenance, is a key issue for utilities to keep their systems running. Awareness on system state is a prerequisite for sound decisions to utilize appropriate maintenance/replacement strategies. Basically, condition monitoring tools are used to assist the network owners in avoiding unplanned outages in the electric power network.
A key function of such tool is to capture signs of
degradation and predict whether possible failure is on hand. Further, it is used to recommend on possible remedial actions to be taken to prevent a breakdown in the system. Condition monitoring comprises three main steps namely data acquisition, data handling and data utilization. In general, the main intention behind a data handling module is to detect abnormalities in the system, associate them with characteristics obtained e.g. from past experiences or from a training dataset and extract the underlying trend. PD events are indications of an ongoing degradation process in the insulation [4]. For an installed MV cable system, partial discharge (PD) measurement is one of the most helpful methods to evaluate the state of the insulation especially when it provides the possibility of pinpointing the PD source. The electromagnetic signals originating from the PD are capable to reach the cable ends where they can be detected [4]. Therefore, information provided through PD diagnostics is invaluable for maintenance activities.
However, such
raw measure is useful only if it is accompanied by an interpretation process. Thus, PDs detected continuously need to be analyzed to extract abstract measures to possibly identify the underlying degradation process as well as the existing status and ideally the remaining life of the insulation media. This chapter explores various steps to identify the insulation system's state based on PD measurement and deduce information from raw PD data.
4.1 Data mining and pattern recognition Implementing the condition based maintenance approach results in a huge data stream which need to be translated to meaningful information (knowledge). However, the extraction of the knowledge is a delicate task [38]. Various techniques 37
38
CHAPTER 4.
PARTIAL DISCHARGE PATTERN RECOGNITION
can be employed to map the low level data to useful information [39]. For example, a proper model can be developed describing the degradation process. Usually, the physical degradation mechanism is complex and only partly understood. Relevant parameters to describe the nature behind the PD phenomena can hardly be attained. Alternatively, models can be considered that describe the dataset in an abstract way.
At the core of this process lay data mining methods for pat-
tern recognition and extraction [39]. Traditionally, such a process relied on human power and was done manually. However, thanks to the advances taken place in the eld of electronic computation, this can be done much faster and more accurate nowadays. For many years, defect identi cation based on PD recognition was done by observing the PD pulses shown on an ellipse representing the power frequency signal on an oscilloscope screen. The advancement in electronic computation and digital monitoring [40], as in other elds, opened up new opportunities for exploiting automated pattern recognition techniques to e ciently manipulate the vast stream of data to diagnose potential de ciencies in insulating media. These methods dier from each other rstly in the way of representing the data and secondly in the approach of classifying the defects.
To properly represent the
data, various approaches such as pulse shape [41, 42, 43, 44, 45, 46, 47, 48, 49, 50] presentation, statistical parameters [40, 51, 52, 53, 54, 55] and statistical distributions [38, 39, 51, 56, 57] are employed. The classi cation of the defects are done through dierent techniques for instance by means of statistical based algorithms [40, 42, 51, 52, 58] or by means of neural networks [59, 60, 61, 62, 63, 64, 65, 66], which are nowadays common pattern recognition techniques.
Generally, for the
classi cation tool, only limited trained PD datasets developed based on a few simple degradation processes from samples in laboratory environments, are available. However, for systems operating under real operational condition the aging mechanism, the aging scale and the failure process are more complex as compared to those under laboratory conditions. Therefore, the developed trained dataset may fail to respond to the real aging process. In case of on-line PD measurement, where a variety of defects with mostly unknown background exist, another approach is being used which is described in the following chapters.
4.2 PD related parameters An important step in PD pattern recognition is to quantitatively [40] describe the ongoing degradation process within the insulating media. To this end, the measured data are either directly used or manipulated to form representative measures for an ongoing process. In this work, the representative quantities are classi ed in two main groups namely basic quantities and derived quantities [42]. The basic quantities involve information that directly can be extracted from the recorded data.
The derived quantities require post-processing the data and may include
choices for their representation. In the following subsections, description of discharge related parameters are introduced.
4.2.
PD RELATED PARAMETERS
39
Basic quantities
q
PD magnitude: PD magnitude ( ) is one of the indicators accompanying aging phenomena in cable insulation. The PD magnitude depends, among other factors, on the size of the discharging source and its location in the insulation [52].
This parameter is usually expressed in picocoulombs [pC]
or nanocoulumbs [nC]. The PD magnitude is symptom of the existence of a defect in the insulation and its level is related to the extend of the damage [67] in the insulation.
Besides, due to the discharge event taking place in
the insulation, the source can grow both over the length and depth of the insulation for instance in the form of electrical treeing. Such growth, both in length and volume, results in changes of the PD pulse magnitude [51]. This can be understood according to the ratio of the defect size to the healthy section of the insulating material. The growth of defect results in increased PD magnitude. Moreover, during the growth of the defect, new small PD sites are created and therefore, new small PDs are initiated during the degradation of the insulation. However, not always the PD magnitude increases while degradation is progressing. For certain defects, it is observed that the PD activity drops or even can cease during the aging time. This behavior can be attributed to changing of the chemical composition of the insulation near the defect changing the electrical characteristics, e.g. carbonization causing a conductive path preventing further discharge activity. Therefore, continuous monitoring of the PD magnitude helps rstly in revealing the existence of the defect and secondly in indicating the state of the insulation and defect growth rate. However, as mentioned earlier, the level of the measured value is in uenced by the location of the defect.
Basically, PD pulses get
attenuated depending on insulating material and length of the cable as well as equipment present in the propagation path [13] while traveling toward the detection sensors. Ring main units in the cable connection reduce signal amplitude especially for frequency content above 1-2 MHz. Substations with huge rail structures have a high impedance and partly block PD signal transfer (For details see [13]).
n
PD number: Number of PDs ( ) measured during a selected time interval is also indicative of a damage and/or deterioration process in the insulation. This value in combination with PD magnitude provides valuable information to be used to identify the state of the insulation as well as the type of the discharging source. The number of PD pulses may increase with the progress of the deterioration. This phenomenon can be ascribed to the fact that the the probability of generation of an initial electron needed to trigger a PD event increases in the deteriorated insulation [68, 69]. Also, the PD inception voltage can drop.
Therefore, PD occurrence increases which can lead to
faster degradation of the insulation.
40
CHAPTER 4.
PARTIAL DISCHARGE PATTERN RECOGNITION
Derived quantities
PD Charge density: According to the IEC 60270 standard [70], PD charge density is de ned as the summation of absolute values of individual apparent
q Tef f
charge magnitudes
from a chosen eective length
measuring time
and normalized both on
discharge regions this is a proper quantity.
Lef f
Lef f and during eective Tef f . For distributed
and
However, when the PDs arise
from a concentrated origin with size less than the chosen eective length, the
Lef f .
charge density becomes dependent on the arbitrarily chosen value of
In
fact, in the practice of power cable diagnostics, this is usually the case since localized cable joints are the most common origins of the PD activity (see e.g. Figure 4.9 related to mapping diagram shown in Figure 4.1). Therefore, in
L
L
this thesis the summation is made over a relative length ( ef f / cable ) which is taken as 1
of the full cable length and the normalization on length is
omitted. Note that this fraction is about a factor ten below the typical overall location accuracy, which is limited mainly by PD signal dispersion during propagation along the cable [13]. However, the reproducibility of the pulse location is higher when PDs arise from a localized defect with magnitude well above the noise level.
As mentioned in Chapter 2, one measurement
corresponds to the duration of a power cycle, i.e.
20 ms.
The eective
measuring time is equal to the number of combined measurements multiplied by the duration of a single measurement.
The optimal value of
Tef f
is
a compromise between the statistical signi cance of the accumulated data, and the time scale over which one would like to observe varying PD activity. Longer averaging time obviously results in smoother statistics (see Figure 4.10). Too long averaging time, however, will disguise temporarily activity occurring during e.g.
an overvoltage situation.
At present, measurements
taken over one hour are combined (60 power frequency cycles when a record is taken every minute). An additional advantage of this time scale is that it corresponds to a typical thermal response time of cables upon changing load conditions. Varying PD activity related to load cycling can therefore still be recognized and distinguished from trends due to aging. Another advantage of hourly based parameters is that it allows cable network owners to perform corrective actions in case there is a high risk PD activity found. Trends can
Ö
be observed in PD activity after already one or a few days of monitoring. The density is taken at position from 1 to 1000) and time
l
t
t
l
which is
m Lef f
(with
m
is discretized according to
location m and time m , the PD density is given by:
Ö
an integer ranging
n Tef f .
For each
P qi, j P Ddens. (lm , tn ) =
X j
where
qi, j
is the discharge magnitude with
the selected length and
j
i
i Tef f, j/Tcycle
(4.1)
indicating the discharges within
the discharges within the selected time range. Usu-
ally, each record is taken just over the duration of one cycle since it needs
4.3.
PD RELATED PATTERNS
41
to incorporate the injected reference pulse. The part covering the injected pulse is eliminated for the actual PD analysis. This introduces a slight de-
j
pendency of the eective time with the index , which is corrected for. The
normalization on
Tef f /Tcycle
results in the unit nC / power cycle .
PD occurrence rate: In IEC 60270 [70], the PD repetition rate is de ned as number of PDs per unit of time.
Since we will exclusively address the
PD activity per power cycle the term PD occurrence rate will be adopted. This quantity is de ned as the number of measured PDs accumulated over
L
L
the relative length ( ef f / cable ) and time
Tef f
and normalized such that a
number per cycle is obtained. As with the charge density parameter, also here the value is not normalized on length. For each spatial and time block:
P ni, j P Docc.rate. (lm , tn ) =
X j
where
n i,j l
i Tef f, j/Tcycle
(4.2)
is the number of PDs accumulated in one record within selected
length m . The normalization on
Tef f /Tcycle
results in the unit number of
PDs per power cycle.
4.3 PD related patterns Patterns aim to visualize the behavior and reveal the underlying trend of the discharging source.
The following subsections present various PD patterns that
are extracted from the basic PD quantities measured via the SCG systems in operation and their corresponding derived PD quantities.
PD mapping diagram The PD mapping diagram is a direct visualization of the PD concentration. is useful to select the crucial locations of the monitored cable connection.
It
This
pattern can be represented in the form of a 2-D diagram (Figure 4.1a), which shows the PD magnitude versus PD location for each individual detected PD event. With continuous PD monitoring a 3-D mapping (Figure 4.1b) can be made, where the third dimension shows the date/time indicating the evolution of the pattern with time.
The mapping example shown in Figure 4.1, representing the data from a
life cable circuit referred to as "Circuit A", will be used throughout this chapter as illustration.
The addition of the PD trend with time is a major advantage
of continuous monitoring above using o-line systems.
It allows distinguishing
regular patterns, caused by e.g. load cycling from sudden or gradual changes in PD activity at a speci c location due to aging and to see trends in these phenomena. The present interpretation of repeated o-line observations with large intervals (months or even years) on a circuit is always hampered by the uncertainty of dealing with regular patterns or with real aging eects.
42
CHAPTER 4.
PARTIAL DISCHARGE PATTERN RECOGNITION
(b) 1500
Discharge magnitude [pC]
Discharge magnitude [pC]
(a)
1000
500
0 0
50
Figure 4.1:
100 150 Location [m]
1500
1000
500 01−Oct−2007 0 0
50
200
100
150
Location [m]
200
01−Sep−2007
a) Circuit A: 2D PD mapping diagram, b) 3D PD mapping diagram
Figure 4.2: a) Loss-free cable model, b) simpli ed model [3]
PD height distribution PD charge magnitude depends on various factors such as conductor size, insulation thickness, type of the insulation, size and the location of the defect [34, 71]. Figure 4.2a shows the well-known model describing a loss-less cable including a defect, where
c
l
represents the local inductance and
a
denotes the local capacitance,
b
and
represent capacitance of the dielectric parallel to the defect and capacitance of
the defect respectively [3]. This model is often simpli ed by replacing the cable segments by their characteristic impedance (Figure 4.2b). For a loss-free cable the characteristic impedance (Z0 ) is:
r Z0 =
l c
(4.3)
The discharge magnitude for the cable is the discharge that is seen over the cable impedance and can be calculated through:
4.3.
PD RELATED PATTERNS
43
100
Pulse count
80 60
40 20
0 0
200
400 600 800 Discharge magnitude [pC]
1000
Figure 4.3: Circuit A: PD height distribution for defective location around 140 m shown in Figure 4.1.
2 q= · Z0
where
q
represents the charge magnitude,
pulse as a function of time
t.
V (t)dt
V (t)
(4.4)
refers to voltage of the discharge
For the SCG system instead of the voltage, the
V (t) Z0 ). Signal loss and detection sensor characteristics is accounted for by equation 3.5.
PD current is measured (
The discharge magnitude is proportional to the voltage and inversely proportional to the cable characteristic impedance. Equal induced apparent charge will give dierent voltage magnitude for cables with dierent
Z0 .
The pulse magni-
tude also depends on the cable type. Thicker insulation implies larger smaller
c
for larger
in eq. 4.3). Similar defects (equal
Z0 , since larger Z0
c
Z0
l
(larger ,
in Fig. 4.2) result in smaller signal
means a lower value for the series capacitor (
b
in Fig.
4.2). Generally, dierent defects give rise to speci c magnitudes of discharges. For instance, magnitudes of corona discharges usually are in the same range [35] if the atmospheric condition of the site and the positioning of the discharge creating defect do not change.
Under stable atmospheric pressure, for a xed discharg-
ing point, the corona is dependent on the applied voltage, meaning that it would remain about the same amplitude for a xed applied voltage. Anyway, PD magnitude can be used to evaluate the insulation status as well as to provide a clue on the defect type [41, 58]. This characteristic parameter can be visualized in the form of a pulse height distribution (PHD) [52] for a defective location along the
44
CHAPTER 4.
PARTIAL DISCHARGE PATTERN RECOGNITION
cable length. Figure 4.3 depicts the PHD pattern for concentrated PDs occurring around the discharge location at 140 m shown in the mapping diagram in Figure 4.1. This pattern can be modeled by statistical distribution models such as Weibull, Gamma and Normal distributions. Parameters of the applied models can be interpreted as indicators of the defect type. In section 4.4, dierent models are applied to the PD magnitude (PHD) pattern and the e ciency of each model in providing information to identify the type of the defect is discussed.
PD charge density pattern PD charge density is a quantity which is derived based on the PD magnitude. This parameter is more objective than the PD magnitude itself, since it takes into consideration the relative measuring time and relative cable length. Basically this quantity is less in uenced by noise impeding the actual measurement. Disturbing signals and peaks in noise may be interpreted as PD events, if they accidentally are detected in the measuring units at both cable sides. However, these occurrences are expected to happen randomly distributed in the mapping and will not lead to speci c features.
On-line measurements enable us to monitor the system for
a continuous period of time. Therefore, patterns are obtained for this parameter over the cable length and over any desired period of time. Trend in this pattern is informative of the stage of deterioration.
The charge density is a measure of
the energy deposited by the PDs to further degrade the insulating material. The charge density pattern can be presented both by means of mean and maximum charge density patterns as discussed in section 4.4.
PD occurrence rate pattern
PD occurrence rate is a quantity which represents the number of measured PDs accumulated over the relative length of 1
of the cable length and time. Similar
as for PD charge density, statistical patterns along the cable length over a certain period of time is created.
Variations in the patterns are a manifestation of the
changes in the deterioration stage of the insulation. These patterns are interpreted as an indication of aging and the underlying trends in these patterns are used to assess the stage of the degradation. In section 4.4, the related statistical quantities are presented.
4.4 Statistical parameters for PD patterns Statistical models applied to PHD The PHD pattern can be modeled by various statistical distributions. These statistical models contain parameters with values that are correlated to the type of the defect where possible. Thus the parameters indicating the magnitude and the shape of the distributions can be used as indicators. This section introduces statistical models to be applied to PHD patterns. Although, all the presented models
4.4.
STATISTICAL PARAMETERS FOR PD PATTERNS
45
can be used to roughly represent the data, some distributions appear to be more practical than others. Trends in the model parameters over time can be used to reveal the progressive degradation occurring in the speci c location.
Normal model - The probability density function for the normal distribution is given by:
f q; µ, σ where
q
2
=√
is the PD magnitude,
1 2π · σ 2
µ
·e
(q−µ)2 − 2 ·σ 2
q∈R
(4.5)
σ
is the variance
is the average value and
that represents the width of the distribution. The PHD pattern for corona discharges can be well modeled by this distribution [72, 73]. Since it describes only symmetrical distributions, it is not used in this work as a general model to represent the PHD pattern.
Gamma model - The density function for Gamma distribution is given by: q
f (q; k, θ) = q k−1 · where
q
is the PD magnitude,
e− θ k θ · Γ (k)
q∈R+
(4.6)
k is the shape factor and θ represents the scale
factor. This model has more exibility in modeling the PD data during dierent stages of the degradation. In [74] it is expressed that the amplitude of the PDs are gamma distributed. However, during the life span of the cable insulation, dierent PD amplitudes, in dierent occurrence numbers are measured.
In case that higher PD magnitudes appear with higher occurrence
rate compared to lower PD magnitudes, then this model fails to provide a
good t since Gamma distributions are right-skewed. Weibull model - Probability density function for this distribution is described by:
β q β−1 −( αq )β · ·e q∈R+ α α magnitude, α is the scale factor and β
f (q; α, β) = where
q
is the PD
(4.7) represents the
shape factor. Weibull distribution is a exible model, that can either be right-skewed or left-skewed.
Such characteristic of this distribution makes it an appealing
option to be used to represent the PHD pattern.
For
has a longer tail meaning that it is right skewed, for
β . 2.6 this model 2.6 . β . 3.7, this
model tends to be centered, and in fact approaches the Normal distribution, for
β & 3.7
this model changes to be left-skewed.
This property makes
the Weibull an attractive option to represent the PD activity from dierent sources at dierent degradation stage of the insulation.
46
CHAPTER 4.
PARTIAL DISCHARGE PATTERN RECOGNITION
−3
x 10
1
Density
2.5 2 1.5 1 0.5 0
0
200
400
600
PD magnitude [pC]
800
1000
(b)
Cumulative probability
PD magnitude Weibull Distribution Normal Distribution Gamma Distribution
3 (a)
0.8
0.6
0.4 PD magnitude Weibull Distribution Normal Distribution Gamma Distribution
0.2
0
0
200
400
600
800
1000
PD magnitude [pC]
Figure 4.4: Models t to PD magnitude: a) probability function, b) cumulative density function -
Circuit A
The models presented through equations 4.5 to 4.7, are applied to the PD data collected from monitoring a live cable circuit to graphically compare their feasibility in providing proper description of the data. For statistical signi cance of the model parameters the distribution should contain su cient data. PDs captured over an hour of measurement are often not su cient for reliably assigning a statistical distribution. Therefore, in our research work a minimum of 25 events is taken to obtain signi cant values for the distributions parameters.
That means
larger time blocks are needed than the ones for the PD charge density and occurrence rate distributions. The presented models are applied to the measurements performed for the PILC cable with mapping diagram shown in Figure 4.1. PDs from a defective location at 140 m are considered for the modeling. Figure 4.4 depicts the ts of the various models applied to the PDs from defect captured over 72 hours. Normal model is not a proper model to t the data, as can be seen in 4.4a, the PD magnitude are not symmetrically distributed, therefore, the distribution tends to move to the negative domain and start from values below zero to better t the higher weighted PDs which are concentrated in the center of the pattern.
This
leads to signi cant deviation since it can not accurately t the low PD values. The Gamma and the Weibull distributions show better t for the data ranging from small to larger PD values. The two models are aligned for small PD values, however, for larger ones Weibull provides slightly closer estimate of the data. As discussed earlier, another advantage of the Weibull is its capability to skew to right or left. Therefore, it is capable to model the patterns where the distribution of the histogram is more concentrated on the right. Figure 4.5 shows a mapping diagram of an example (referred as Circuit B) where larger PDs are appearing in larger number as compared to the smaller ones. PHD pattern is made for the PD magnitude captured from the location of the defect and the pattern is modeled
4.4.
STATISTICAL PARAMETERS FOR PD PATTERNS
47
1400
Discharge magnitude [pC]
1200 1000 800 600 400 200 0 0
50
100 Location [m]
150
200
Figure 4.5: PD mapping for Circuit B
by the Weibull and Gamma distributions (Figure 4.6). By visually comparing the two distributions, one can deduce that the Weibull is a better choice to represent this pattern. Figure 4.6b shows the cumulative distribution model including the 95% con dence interval.
The Weibull distribution including its con dence level
represent the experimental PD magnitude curve better than the Gamma distribution.
Therefore, the Weibull model is taken to represent the PD magnitude
pattern. In [4, 41, 58, 75, 76, 77, 78] it is shown that the Weibull scale and shape parameters tend to be a good indicator of the defect type and, in fact, it is noted that the shape parameter of this model is correlated to the main defect types namely, internal, surface and corona discharges. It is shown in [58] that generally the value of the shape parameter is internal discharges and
β&8
β .2
for surface discharges,
2.β .8
for
provides indication of corona discharges. A trend in
this parameter can be used as an indicator for change of the degradation status. In addition to the Weibull parameters' values, their con dence bounds must be determined to indicate the reliability of the estimates. Basically, due to inherent statistical uctuations, but also due to the fact that it is only an assumption that the PD magnitude can be modeled by a Weibull distribution, there is an uncertainty in the estimated parameters. There is no underlying physical reason that PDs should follow precisely the Weibull or any other distribution.
In this
work, Con dence Interval (CI) with a 95% con dence level (δ ) estimated by the Fisher matrix [79] is used. Details are discussed in Appendix A. Figure 4.7 and Figure 4.8 show the parameter of the Weibull modeling over the complete length of the cable and the location of the defect versus time corre-
48
CHAPTER 4.
PARTIAL DISCHARGE PATTERN RECOGNITION
−3
x 10
3.5
Density
3
1 PD magnitude Weibull Distribution Gamma Distribution
Cumulative probability
4
2.5 2 1.5 1
0.8
PD magnitude Weibull Distribution Weibull 95% confidence bounds Gamma Distribution Gamma 95%confidence bounds
0.6
0.4
0.2
0.5 0 0
200
400
600
800
1000
1200
PD magnitude [pC]
0 0
200
400
600
800
1000
1200
PD magnitude [pC]
Figure 4.6: Comparison of Weibull distribution and Gamma distribution -
B
Circuit
Figure 4.7: Weibull model applied to PD magnitude for Circuit A: a) normalized scale parameter (α), b) shape parameter (β )
sponding to Figure 4.1, respectively. In the patterns presented in Figures 4.1 and 4.7, three distinct discharging location are distinguishable: and two distributed around 200 m.
one around 140 m,
As it is mentioned in Figure 4.7, the joint
located at around 140 m, was replaced at certain time due to the critical condition revealed by PDs captured via SCG. After this, the suspected joint was subjected to a DC test for further assessing of its condition, which indeed resulted in its failure, con rming its lowered insulation capability. The values of the scale and shape factors (Figure 4.8) indicated that the location around 140 m suered from
4.4.
STATISTICAL PARAMETERS FOR PD PATTERNS
4
Beta parameter
Scaled Alpha parameter [pC]
1
49
0.5
3
2
1
Scaled Alpha paramterer Trend line
0 01−Sep−2007
01−Oct−2007
0 01−Sep−2007
01−Oct−2007
Date−Time
Date−Time
Figure 4.8: Weibull parameters for PD magnitudes from defective joint located at about 140 m, a) normalized scale parameter, b) shape parameter - Circuit A.
an intense internal discharge source. The (normalized) scale parameter showed on average an increasing PD level.
Descriptive statistics PD charge density and occurrence are indicative for the degradation process. The values can be calculated for PD pulses that are measured during each power cycle. Both mean and maximum values accumulated in a certain time block are used. In this work, all the derived quantities are scaled to values between 0 and 1, i.e. 0 symbolizes zero and 1 symbolizes the maximum value in the related pattern. Equations 4.1 and 4.2, denote the average PD charge density and occurrence rate. Alternatively, also the maximum PD charge density and maximum PD charge occurrence rate seen in one of the power frequency cycles within taken to characterize the insulation state.
Tef f
can be
These quantities retain information
on occasional high activity in contrast to the averaged values over many cycles. Equations 4.8 and 4.9 show the related maximum values on position
t.
P qi, j
/Tcycle
ef f, j
P Dmax.occ. (lm , tn ) = max j T
and at time
i
P Dmax.dens. (lm , tn ) = max j T
l
P ni, j
i
/Tcycle
ef f, j
(4.9)
The function "max" takes the highest value with respect to index
(4.8)
j,
i.e. the
highest value that has occurred during the chosen time block (for instance one hour) for each 1
cable length.
50
CHAPTER 4.
PARTIAL DISCHARGE PATTERN RECOGNITION
01−Oct−2007
6000 1
(b)
5000 23−Sep−2007 4000 16−Sep−2007
3000 2000
09−Sep−2007 1000 01−Sep−2007 0
50
100
Location [m]
150
200
Figure 4.9: PD charge density pattern (1-hourly basis) for Circuit A: a) Mean value, b) Maximum value.
Figure 4.9 shows the normalized density pattern based on mean and maximum PD charge density. The main defect of this cable circuit, located at around 140 m, shows intense PD activity in both patterns. In the mean density pattern, discharge activity at the very beginning is highest while the pattern with maximum values shows that the highest discharging level just before the replacement took place. This dierence can basically be attributed to the fact that the PD activity in terms of number of PDs in the early stage is higher, giving more weight to the density while in the last stages, the number of PDs decreased while their magnitudes increased.
Such behavior itself is an indication of change in the status of the
insulation.
PDs distributed along the cable length (see Figure 4.1) are mostly
averaged out. At both cable ends, PD spread over 10 m from the terminations are distinguishable, however, with lowered level. At 180 m some relatively large PDs occurred spread over a region of 20 m (see maximum pattern), these activities are lowered in the mean pattern, since these discharges do not repeat. Figure 4.10 shows the normalized PD charge density patterns averaged over 8-hour and 24-hour time blocks respectively.
As can be seen from the patterns
the random PD activity as well as the distributed ones are partly averaged out. PDs spread around 0 m and 180 m are averaged out partly in 8-hourly pattern and almost completely in 24-hourly pattern due to averaging over a longer time. Therefore, more clear patterns are created for defect identi cation. Longer averaging time may result in obtaining more clear pattern with more visible PD activity from real defects, especially when PDs are distributed along the cable length. However, in some cases, PD activity may rise for a very short period of time and disappear, especially when the defect has reached the last stage before the breakdown occurs. Some PD activity can only show very low level while still being crucial for the insulation. Too long averaging time may result in averaging out those activities as well as losing time in taking action for possible corrective
0
4.4.
STATISTICAL PARAMETERS FOR PD PATTERNS
Figure 4.10:
51
PD charge density pattern for Circuit A: a) 8-hourly pattern, b)
24-hourly pattern.
remedy.
Therefore, as explained earlier in this chapter, 1-hourly time block is
selected for analyzing PD patterns. Figure 4.11 shows the normalized mean and maximum PD occurrence values. The defective joint is clearly visible in both patterns. By averaging the values over time, prominent PD activity stands out, whereas noise or spread activities merge with the background. Mean patterns, rules out the discharge activities that are of low occurrence. This helps to distinguish locations with high PD activity from the background. Especially for PILC cables there is always a broad background of low magnitude PDs. These PDs, which might be generated between the cable cores in a belted cable, are usually considered to be harmless to the insulation.
Figure 4.11: PD occurrence rate pattern (1-hourly basis) for Circuit A: a) Mean value, b) Maximum value.
Chapter 5
Decision Support System for Smart Cable Guard In chapter 4, basic patterns that can help to reveal the status of the insulation media have been discussed.
These patterns aim to visualize the potential mal-
functioning within the system through projecting the suspected PD activities along cable length and time.
However, analyzing the presence of the defects is still
dependent on visually inspecting the patterns by specialists. The fast growth in the number of installed Smart Cable Guard (SCG) units in the eld, makes it virtually impossible to individually investigate all patterns for each circuit. This calls for developing an automated tool to analyze the circuits and in case of malfunctioning in any circuit, to perform estimations on the state of the insulation. Depending on the insulation state, this information is communicated in the form of a logged message or in the form of an acute alarm. A so-called decision support system is developed to enhance the capability of the SCG system by adding a status analyzer module to the existing system for assessing the cable insulation condition. Such a tool includes several sub-tools to process the data
to eliminate noise or non-PD data to identify potential defects to calculate their failure probabilities and correlate them to a risk index
Figure 5.1 depicts the process from data acquisition to assessing the risk level(s) of identi ed defects related to the insulation condition. The mentioned sub-tools are described in details in the following sections.
5.1 Noise Reduction Algorithm Raw PD data (i.e.
PD magnitude and number of PDs) are pre-processed and
converted to characteristic quantities, namely PD charge density and PD charge occurrence rate. Patterns presenting these PD-related values have been introduced 53
CHAPTER 5.
DECISION SUPPORT SYSTEM FOR SMART CABLE
54
GUARD
Figure 5.1: Decision support module for SCG system
in the previous chapter. By visually examining the patterns, one may notice that in some circuits the measured data and the associated values are distributed over a wide region along the cable system. These signals occur either from distributed low PD activity which is harmless in case of paper-oil insulated cables, or arise from noise picked up by the cable connection.
To maximize PD sensitivity, the
SCG detection level can be remotely lowered until misinterpreted signals as PDs just start popping up. However, external conditions may vary, causing occasional high background of misinterpreted signals referred to as noise. Figure 5.2 shows examples of mapping diagrams of PD measurements for two live circuits addressed as "Circuit A" and "Circuit C". In both circuits intense PD activities as well as background noise are observed. The PD activity in Figure 5.2a at the location of a defect is clearly distinguishable from the background activity. The PD activity in Figure 5.2b, is spread over the whole length of the cable circuit. On top of the
1500
6000 (b) Discharge magnitude [pC]
Discharge magnitude [pC]
(a)
1000
500
0 0
50
100 150 Location [m]
200
5000 4000 3000 2000 1000 0 0
2000
4000 Location [m]
6000
Figure 5.2: PD mappings diagram - a) Circuit A - 214 m PILC cable, b) Circuit C - 7 km XLPE cable
5.1.
NOISE REDUCTION ALGORITHM
55
background, three concentrations near 500 m, 4500 m and 5000 m are present. Their observation is hampered by this broad noise background. The e ciency of any algorithm that is utilized to automatically identify potential defects will be impeded by a high background. Therefore, it is necessary to perform noise reduction prior to any further data processing. However, noise reduction may not eliminate undesired data completely or may reject data from PDs. The noise reduction in this research work is performed through applying a statistical approach.
First step is to de ne a threshold based on the statistics
of the measured data levels observed over the length of the cable in a certain time period. Knowing that defects that generate PDs are normally concentrated in localized regions only, then the next steps involve labeling data that does not reproduce themselves in the close spatial vicinity and/or over a time as "passive data" and exclude them from further processing.
In the following sub sections,
details of the noise reduction algorithms are presented. Throughout this chapter the performance of the algorithms is exempli ed by two cases.
"Circuit A" is a relatively short PILC cable, showing clearly con-
centrated PD activity.
"Circuit C" is a 7 km XLPE connection where a broad
background conceals the local PD concentrations.
Threshold based on running average A preliminary noise reduction is achieved by thresholding based on employing a moving average. In statistics a moving average is de ned as a nite impulse response lter used to analyze a set of data points by creating a series average of the full data set. The moving average is an approach to smoothen the data set. This section describes a practical tool developed based on moving average to de ne a preliminary threshold for the data set to discard potential noise.
Due to
the fact that all data points distributed along the cable length are assumed to have similar weights, simple moving average (SMA) is preferred over other moving average techniques (cumulative, weighted and exponential moving averages). Algorithm 5.1 presents the preliminary noise reduction through thresholding based on computing an average of a stream of numbers by averaging over
n
elements
from the stream. Next, the pre-processed measurement data is provided as input for thresholding based on levels assigned by the moving average algorithm. The moving average runs for data over the cable length for each single time block and sets the threshold value in accordance to the data level observed in that time block. The location averaging window (LAW), i.e. the length that the averaging is performed over, is an important factor in the calculation of moving average. Its value is estimated based on an experimental curve developed according to observed dispersion of defects. PDs are measured for each fractional time (FT). Fractional time for PD signals is de ned in relation to the signal propagation time of the whole cable circuit and it is expressed as an integer between 0 and 1000. That means that the length of the cable is scaled to a value between 0-1000. Therefore, defects from shorter cables are more distributed while discharges in longer cable
CHAPTER 5.
DECISION SUPPORT SYSTEM FOR SMART CABLE
56
GUARD
Defect dispersion (number of FTs)
30 FTs vs Cable length Fitted line 95% confidence bound
25 20 15 10 5 0 −5 −10 0
2000
4000
6000
8000
Cable length [m] Figure 5.3: Empirical defect dispersion curve - Fractional time vs. cable length
are limited to a smaller region in terms of FTs.
PDs from cables with various
lengths have been studied, and the dispersion of their defects over the fractional time has been measured. The defect dispersion is plotted vs. the cable length in Figure 5.3 and a line is tted to the data. The 95% upper bound value is utilized further to calculate the averaging windows for dierent cable lengths. Figure 5.4 illustrates pre-processed data in a single time block calculated for measured data from Circuit A and Circuit C and the calculated moving average curve for that particular time block. The moving average forms the threshold line and data below this line are to be excluded from the data set.
Figure 5.5 de-
picts the result of thresholding for clear defect locations as well as for arbitrarily chosen locations along the same cable without a clear PD concentration. As can be observed from top-set in Figure 5.5, it is inevitable not to lose PDs from the defect in this process.
At arbitrary locations (Figures 5.5, bottom-set), part of
the noise signals is discarded. Thresholding applied to the locations with background activity, only eliminates the lowest values which contain a relatively high fraction of incorrectly assigned PDs. Still further processing is needed to discard the remaining background noise as much as possible.
Noise Reduction by Percentile thresholding The pre-processed data are smoothened by applying the preliminary noise thresholding. The data needs to be processed further to eliminate data that can adversely in uence the e ciency of an automated defect identi er tool.
The secondary
thresholding algorithm aims to minimize the in uence of the noise. Pre-processed datasets include values which are considerably distant from majority of the values in the dataset. In statistics these are referred as outliers; points that appear to deviate markedly from other members of the sample in which they occurred [80]. These outlying values can appear in PD dataset as a result of mis-
5.1.
NOISE REDUCTION ALGORITHM
Algorithm 5.1 Noise reduction based on moving average thresholding
57
CHAPTER 5.
DECISION SUPPORT SYSTEM FOR SMART CABLE
58
GUARD
100 PD−related value Moving average line
PD−related parameter
(a) 50 40 30 20 10 0 0
50
100
150
PD−related value Moving average line
(b)
PD−related parameter
60
80 60 40 20 0 0
200
2000
4000
6000
Location [m]
Location [m]
Figure 5.4: PD-related data for a certain time block and moving average thresh-
PD−related value Moving average line
50 40 30 20 10 0
130
135
140
PD−related parameter
PD−related parameter
olding - Circuit A and Circuit C
40 30 20 10 0
145
PD−related value Moving average line
50
400
Location [m]
PD−related parameter
6 5 4 3 2 1 0
200
205
600
700
210
Location [m]
PD−related value Moving average line
20
PD−related parameter
PD−related value Moving average line
7
500
Location [m]
15 10 5 0
2500
3000
3500
4000
Location [m]
Figure 5.5: Thresholding for Circuit A (left) and Circuit C (right); Top: location of a defect, bottom: arbitrary location
5.1.
NOISE REDUCTION ALGORITHM
59
measurement, recording disturbing signal, capturing noise, etc. However, extreme outliers can also be a sign of sudden change in the system condition. There are techniques available to detect them and if appropriate remove them from the data set.
One of the robust statistical techniques is to de ne a lower and an upper
th
range based on rst quartile (25
percentile) and third quartile (75
th
percentile).
In this technique the lower and higher bounds are calculated according to:
limL = Q1 − 1.5 (IQR) limU = Q3 + 1.5 (IQR) th
where Q1 and Q2 are the 25
th
percentile and 75
(5.1)
percentile respectively. The in-
terquartile range (IQR) is a measure of statistical dispersion and is de ned as:
IQR = Q3 − Q1
(5.2)
All values of the dataset which are not within the de ned range, will be labeled as outliers and excluded from the data set. This technique can not be utilized in this form for our research work, because extreme outliers can originate from a defect. Therefore, only a lower limit is included. Besides, if the dataset is extremely noisy, the lower limit will be in uenced by the weight of the noise data and as a result it would not be eective in cutting o the noise.
Therefore, the lower limit is
de ned based on the rst quartile rather than its combination with interquartile range.
The lower limit forms a thresholding line and any data below that level
is eliminated. Algorithm 5.2 shows the approach to noise reduction based on the calculated percentile. Since this PD activity may not repeat itself often, or may only appear for a limited duration and then cease, it is important to retain these data. Therefore, a module is included to check whether the noise reduction can be applied or not.
The cable circuit is evaluated to check whether more than
10% of the cable length is discharging. If so, the data processed based on moving average are provided for further noise reduction based on percentile, else the data set is analyzed as described in the next section. Figure 5.6 illustrates how these thresholds are set to eliminate the undesired data for Circuit A and Circuit C for a particular time block.
Noise Reduction by detecting PD clusters After applying the thresholds either moving average or percentile line, there may remain still data which did not originate from the defect, especially for a dataset where percentile thresholding algorithm was omitted. To eliminate those data, the data are further processed through the sub-tool addressed as "micro-clustering" tool. This tool tries to distinguish PDs from noise by detecting clusters for data in close vicinity. These clusters are referred to as micro-clusters. They are created for each single time block.
Algorithm 5.3 presents the designated approach.
In
this approach, re ned data from moving average or percentile base noise reduction are further processed by comparing the existence of activities in the vicinity of a certain location. The close vicinity is de ned based on the defect dispersion curve.
CHAPTER 5.
DECISION SUPPORT SYSTEM FOR SMART CABLE
60
Algorithm 5.2 Noise reduction by applying percentile thresholding
GUARD
5.1.
NOISE REDUCTION ALGORITHM
61
100 PD−related value 25th Percentile
0.8 0.6 0.4 0.2 0
0
50
100
150
200
Location [m]
(b)
PD−related parameter
PD−related parameter
1 (a)
PD−related value 25th Percentile
80 60 40 20 0 0
2000
4000
6000
Location [m]
Figure 5.6: Thresholding based on 25th Percentile a) Circuit A b) Circuit C
The algorithm runs for data measured in a single time block. If other PD data appears in the vicinity of a data point, then they are uni ed and form a micro cluster, else, the data will be labeled as "passive" and will be stored. In case that some activity starts later at the same location, then the passive data would be taken into consideration for further analysis by labeling it as "active". Figure 5.7 shows the result of micro-clustering over length of cable for Circuit A. Inset of Figure 5.7 shows a zoomed view of the clusters at the location of the defect. Each block is a microcluster formed for PD related parameters (shown as dots in each block) in a single time block. In later stages micro clusters that are repeating over time will be combined, if possible, to form a cluster which can be assigned to a potential defect site (see, next section).
Figure 5.7: PD related pattern after applying micro-clustering - Circuit A
CHAPTER 5. 62
DECISION SUPPORT SYSTEM FOR SMART CABLE GUARD
Algorithm 5.3 Micro-clustering algorithm for noise reduction and defect identi cation
Figure 5.8 and 5.9 show the applications of the noise reduction algorithm to the PD-related patterns for Circuit A and Circuit C respectively, before and after noise reduction. In Figure 5.8, the defect locations remain clearly visible. The PDs from the termination at the far end has been reduced slightly. Figure 5.9 illustrates the performance of the algorithm in reducing the background in a heavy noisy environment. The noise reduction approach manages to reduce the noise contrib-
5.2.
DEFECT IDENTIFIER
63
Figure 5.8: a) PD charge density pattern a) before and b) after noise reduction Circuit A
ution to further analysis while keeping the localized PD concentrations almost unaected.
5.2 Defect identi er PD activity manifests through peaks of repetitive discharges along the cable length over a period of time.
The remainder of the data observed in the patterns are
either noise or discharges initiated due to the type of the cable insulation itself (in PILC cable due to the spaces between the paper layers) which are considered non-hazardous for the insulation integrity. Generally, a more intense peak is an indication of a more severe impairment. As mentioned earlier in this chapter, to
CHAPTER 5. 64
DECISION SUPPORT SYSTEM FOR SMART CABLE GUARD
Figure 5.9: a) PD charge density pattern a) before and b) after noise reduction Circuit C
relax the dependency on experts interference for identifying defects within the insulation, it is necessary to provide the measuring system with a tool to perform defect identi cation automatically. In this section algorithms that have been employed to capture discharge peaks are described, the cons and pros of each algorithm are discussed.
Peak identi er PD peaks originated from defective locations tend to have a bell-shaped curve. Figure 5.10 depicts an example of a pattern created for PD charge density obtained for Circuit A. The Normal distribution is exploited as a peak identi er shape:
5.2.
DEFECT IDENTIFIER
65
PD charge density [pC / power cycle]
1
PD charge density [pC / power cycle]
1
0
0
50
100 Location [m]
150
0
200
Location [m]
Figure 5.10: Cumulative PD charge density pattern - Circuit A
(x −µ)2 f x ; µ, σ 2 = A · e− 2 σ2 where
x
denotes the location,
A, µ, σ
(5.3)
are the model parameters.
The developed algorithm is applied to the PD charge density and the PD charge occurrence rate (average and maximum values).
It estimates the parameters in
(5.3) by applying the least square technique. The goodness of the t is examined by employing the R-square test. R-square is a statistical parameter that presents how well a dataset is approximated by the provided model. Its value varies between zero and one; higher value implies better t. This value is calculated according
n P
R2 = 1 −
i=1 n P
2
(yi − fi )
(5.4)
(yi − y¯ )
2
i=1
yi represents PD related values, fi denotes the associated model values, and y¯ represents the mean value of input PD in certain location block respectively. 2 2 The acceptance value for the R test is taken as 0.9, i.e. ts with R < 0.9 will be
where
rejected. Algorithm 5.4 illustrates the peak identi er tool. The PD-related data in each time block is provided as input for the peak detector. The algorithm shifts over the cable length to identify potential defective locations. If the model ful lls the requirement for goodness of t test, the location is labeled as potential defect. The algorithm also iterates through time blocks. If peaks repeat themselves over consecutive time blocks, then the peak information will be labeled as an active defect and the information will be provided for failure probability analysis, else the peak information will labeled as passive and stored. As discussed in the next subsection, it can be re-assigned to active if at the same location PD activity occurs in a later stage.
CHAPTER 5.
DECISION SUPPORT SYSTEM FOR SMART CABLE
66
Algorithm 5.4 Automated peak identi er
GUARD
DEFECT IDENTIFIER
67
Discharge magnitude [pC]
(a) 1500 1000 500 01−Oct−2007 0 0
50
100
150
Location [m]
200
01−Sep−2007
PD charge density [pC/power cycle]
5.2.
(b) 1
0 0
50
100
150
200
01−Oct−2007 21−Sep−2007 11−Sep−2007 01−Sep−2007
Location [m]
Figure 5.11: a) PD mapping diagram, and b) Defect detection by Peak Identi er Algorithm applied to PD charge density - Circuit A
Figures 5.11 shows the application of the peak identi er algorithm to the PD charge density for Circuit A. Three defective locations identi ed by the algorithm also manifest as intense discharging locations on the mapping diagram shown in Figure 5.11a. The discharge activity at the location from the termination at the left side (0 m) to about 8 m from the termination, is less intense as compared to the other locations and does not repeat itself as often as the activities from two other location do. However, still the algorithm captures them. Two other defects are clearly identi ed by the algorithm. At location around 140 m, the mapping diagram shows increased PD activity, while the PD charge density pattern shows a decay over time. This occurs due to the fact that each normal curve versus time represents the average PD charge density over the current and all previous time blocks. The decaying trend in t model can be used as a basis for risk of failure assessment. Figure 5.12 shows the result of applying the algorithm to Circuit C with high noise level. The algorithm identi ed 6 discharging locations which are magni ed in the insets of Figure 5.13. Out of these 6 locations in fact 5 locations (around 500 m, and 3500 m-5000 m) were identi ed as real defects. The drawback of the algorithm is its sensitivity to the threshold value set for accepting/rejecting the hypothesis of the t representing the PD data. Generally, there is no strict value to accept the model. The higher the value, the better the t is. However, fair judgment is also dependent on the sample size provided for modeling. In this work, the value is set to 0.9. Another drawback of the approach is that the algorithm is sensitive to how distributed the defect is.
If the defect
is concentrated sharply at a speci c location, the model tends to fail due to the rejection of the t hypothesis. Figure 5.14 shows an application of the algorithm to the measurement data from a cable circuit where PDs are distributed over a very narrow region. As can be seen from the result of the modeling, the curve be-
CHAPTER 5.
DECISION SUPPORT SYSTEM FOR SMART CABLE
68
GUARD
PD charge density
1
0
02−Aug−2008
01−Aug−2008
0
1000
2000
3000
4000
5000
6000
7000
Location [m]
Figure 5.12: Application of the Peak Identi er tool to measurement result - Circuit C
PD charge density [pC/power cycle]
1
0 0
1000
2000
3000 4000 Location [m]
5000
6000
7000
Figure 5.13: Discharge location identi ed by Peak Identi er Algorithm - Circuit C
5.2.
DEFECT IDENTIFIER
69
Discharge magnitude [pC]
1500 1000 500
01−Sep−2008
0 0
1000
2000
24−Aug−2008 17−Aug−2008 10−Aug−2008 3000
Location [m]
4000
01−Aug−2008
PD charge density [pC/power cycle]
1
(a)
(b) 1
0
01−Sep−2008 21−Aug−2008
0 0
11−Aug−2008 1000
2000
3000
4000
01−Aug−2008
Location [m]
Figure 5.14: a) 3D Mapping diagram b) Peak detection - inset shows the front view for part of the t.
comes so sharp and contains only a few points (see inset in Figure 5.14b), too few to form a complete curve. This may result in a rejection during the goodness of t test, and consequently failure in capturing an active discharge source. For Figure 5.14b the constraint was set to
R 2 > 0.7
to obtain a result; with the constraint of
0.9 the algorithm failed to present a curve at the location of the defect.
Sequence Clustering Approach An alternative algorithm to capture a potential defect is referred to as sequence clustering.
Generally, the clustering algorithm aims to eectively capture and
group data that potentially originate from the same source.
Various clustering
techniques have been widely used in various elds such as machine learning [81], pattern recognition, image processing [82], etc. Finding clusters actually indirectly implies nding the potential defect, therefore, it is of great importance to develop an algorithm which can eectively and e ciently discover the potential PD clusters. It has been pointed out earlier how to statistically identify micro-clusters for noise reduction over the length of the cable.
In fact, the same algorithm is adopted here, but now as a function of
time. The sequence clustering tool uni es repeating micro-clusters over consecutive time blocks. Algorithm 5.5 depicts the procedure to form a cluster for data from a potential defect. If the sequence clustering algorithm observes micro-clusters overlapping with clusters or micro-clusters from previous time blocks, then it combines them as a single cluster and updates the representative values. The algorithm looks back in time for (micro-) clusters during a pre-assigned period of time, e.g. three days. If no (micro-) clusters around the same vicinity are observed during the assigned period then it labels the (micro-) cluster as passive and stores it in the database.
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Algorithm 5.5 Sequence clustering for defect identi cation
Representative parameters for each cluster are de ned as:
Total PD max./mean charge density per cluster per time block Total PD max./mean charge occurrence rate per cluster per time block PD cluster width Trend in average PD charge density and PD occurrence rate
5.2.
DEFECT IDENTIFIER
71
Figure 5.15: Defect detection by applying PD sequence clustering to density pattern shown in Figure 5.8b - Circuit A
The total PD max./mean parameter for PD charge density and occurrence rate are summations of the values observed in that cluster. The PD cluster width is tricky to de ne due to the fact that the clusters do not always originate from exact the same location but possibly from close vicinity and do not necessarily overlap or align. This factor is crucial for risk estimation of the cable insulation. Basically, clusters with low level of discharges spread over a wide region are less harmful as compared to those with higher concentrated PD activity. To estimate the cluster width, the criterion is de ned as calculating the mean and standard deviation of the location of the clusters observed in close vicinity over time; the width of the clusters is taken
4σ
to include both (micro-) clusters. Trend is de ned as average
changes of the PD charge density and occurrence rate parameters per each cluster take place over a de ned time (in this work 3 days).
Basically, the average of
the PD values is calculated for the last three days and it is subtracted from the average of the PD values over the three days before that. Figure 5.15 depicts the application of the sequence clustering algorithm to the pattern created for processed data measured for Circuit A. Comparison of the patterns shown in Figures 5.11 and 5.15, shows that the clustering approach works satisfactory in capturing the microclusters either from a real defect or from nondefective sources. As can be seen from Figure 5.15, three active PD sources are captured nearby both terminations and one at the location around 140 m. The cluster formed in the region 190 m - 200 m shows that microclusters that appear after a period of time, are re-assigned active and uni ed in form of a cluster. The e ciency to capture a repetitive discharge location in noisy datasets is examined by applying the algorithm to results obtained for Circuit C. Figure 5.16 depicts the clustering performed for Circuit C. Comparison of the clustering with
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Figure 5.16: Defect detection by applying sequence clustering to the density pattern shown in Figure 5.9b - Circuit C
initial PD related pattern, emphasizes that the algorithm manages to capture the potential defective locations in noisy data sets as well. The sequence clustering algorithm is preferred over the normal curve tting approach, because of its higher robustness, higher accuracy, and in fact it is a fast approach reducing the computation eort.
5.3 Risk Indexing Recognition of a defect in an insulation system alone is not su cient to decide upon the necessity and urgency of maintenance. Knowing its consequences would assist in eective decision making with regard to maintenance/replacement strategies. Risk analysis on the severity can be utilized for optimizing the remedial action. In this section the preliminary risk analysis is presented using the data obtained for each cluster during the defect identi cation process. In formation of clusters, several representative values are assigned for each cluster. These values, addressed from now on as preliminary risk factors, are provided as input for risk indexing. The preliminary risk factors are:
Total PD max./mean charge density per cluster per time block Total PD max./mean charge occurrence rate per cluster per time block PD cluster width Trend in average PD charge density and PD occurrence rate
5.3.
RISK INDEXING
73
The most widely accepted approach for risk quanti cation is de ned as: the magnitude of the risk is equal to probability of occurrence of an event multiplied by the severity of that event. So, obtaining the probability of the occurrence of an event is an important step in identifying its associated risk. Determining the rate of occurrence of an event is hampered because of lack of statistical information available on critical values for possible failures. In this thesis, the probability of
F ) for each cluster (potential defect), which will be referred as
defect occurrence (
secondary risk factors, is calculated by applying the Logistic function [83] to the preliminary risk factors. Logistic regression is expressed by means of the logistic function:
F (z ) = where,
F (z) z
(5.5)
denotes the probability of a particular cluster quantity and varies
from 0 to 1 according to parameter Parameter
1 1 + e−z
z.
is de ned by means of a variable
a sensitivity factor
r
x,
having a turn-over value
x0 ,
and
according
z = r · (xi − x0 ) The regression coe cient
r
(5.6)
is calculated based on the Logistic curve slope.
The secondary risk factors form the foundation for calculating the associated risk index for every potential defect, which consequently results in the decision of whether a discharging source considered to be a defect and if so, gives an indication on its severity. Not always all chosen identi ers result in consistent values presenting a defective site. Sometimes, all identi ers are actively representing defects, but in some cases, only one or two are providing positive identi cation. The risk index must be based on the combination of available identi ers. The dispersion of the measured data over the length of the cable is also informative on defect severity. For instance, if discharge peaks appear repeatedly sharp above a certain location, the severity is considered higher than when discharges are spread over a larger region.
Therefore, the width of the captured clusters
(potential defect) is included in the risk calculation. Another important factor in risk identi cation is the underlying trend for a certain observed risk factor. Trend analysis reveals information on whether the levels of the indicators have increased or decreased over time and if they have, the rate these changes have occurred [84]. This would ideally provide information to estimate future activity. Therefore, apart from the risk factors that are being considered in the risk assessment, it is important to consider their associated time trends in the analysis as well. In this research, a relation for risk indexing (RI), is adopted by combining the secondary risk factors:
RI =
1 − (1 − Fdens ) · (1 − Focc.rate ) · (Fwidth ) · (Ftrend−dens )
(5.7)
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Fdens represents the probability of occurrence for certain PD charge density, Focc.rate represents probability of occurrence for certain PD occurrence rate, Fwidth
where
denotes the probability of occurrence of a PD cluster with certain width, and
Ftrend−dens
addresses the probability of occurrence for certain trend in charge
density per cluster. Algorithm 5.6 illustrated the designated approach for identifying the risk level(s). Here, only the most in uential identi ers are incorporated for risk approximation. This is not to say that the other identi ers (i.e.
the maximum values) are not
important, but they are somehow already included by their in uence on the mean calculated parameters. This equation can be extended to include all PD representators. Relation 5.7 projects the potential risk on a scale between zero and one. Probabilities of occurrence are estimated through models that are created based on experimental data. A larger value of the risk index indicates a higher risk that the insulation is exposed to. Accordingly, the obtained quantity can be transferred to Low, Medium or High risk to qualitatively illustrate the condition of the insulat-
Algorithm 5.6 Risk indexing algorithm
5.3.
RISK INDEXING
75
1
1
0.9
0.8
0.8
0.7
0.7
Probability
Probability
(a) 0.9
0.6 0.5 0.4
0.6 0.5 0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
200
400
600
800
1000
1200
1400
1600
0
1800
(b)
0
1
PD charge density [pC / power cycle] 1
0.9
0.8
0.8
0.7
0.7
Probability
Probability
3
4
5
6
7
8
1 (c)
0.9
0.6 0.5 0.4
0.5 0.4 0.3
0.2
0.2
0.1
0.1 0
5
10
15
20
25
30
35
40
PD cluster width [0.1% of cable length]
(d)
0.6
0.3
0
2
PD charge occurence rate [# / power cycle]
0
0
10
20
30
40
50
60
70
PD Trend [pC / power cycle]
Figure 5.17: General probability of occurrence (S-Curve) models: a) PD charge density, b)PD charge occurrence rate, c) PD cluster width, d) PD trend
ion, meaning that values close to 1 represent a high risk situation, while values close to zero can be tagged as low risks. Figure 5.17, shows the general models developed for probability of occurrence for each set of preliminary risk factors. The scaling of the models are done based on experimental data. The critical values are speci c for the insulation type. For instance, critical values for XLPE cable are dierent from those for PILC cable. The discharge level that can be disregarded for the PILC cable, might be harmful for XLPE cable.
Low critical values may lead to unnecessarily activation of an
alarm, and a high setting may result in missing a true warning. Therefore, the models for risk analysis must be provided based on the insulation type if available. Probability of occurrence for each preliminary risk factor is calculated based on the presented models, and the outcomes were provided for Risk index calculation
80
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GUARD
1.01 (a) 1.005
Risk level
1 0.995 0.99 0.985 0.98 01−Sep−2007
1
11−Sep−2007
21−Sep−2007
(b)
Risk level
0.8 0.6 0.4 0.2 0 01−Aug−2008
11−Aug−2008
21−Aug−2008
01−Sep−2008
Figure 5.18: Risk Index variation for the defect at the location a) 140 m - Circuit A, b) 500 m - Circuit C
by applying Equation 5.7. Figure 5.18 displays the RI variation for the two clusters formed at the location of about 140 m for Circuit A, and 500 m for Circuit C. For both locations the clusters with high risk values are assigned. For Circuit A the risk is high as from the start of the diagnosis and for Circuit C a change from low to high risk occurred during the diagnosis. Both locations were indeed defective and suered from intense PD activity. The RI is estimated through the logistic curve model which provides the probabil-
5.4.
PERFORMANCE INDEX
77
ity of an event based on the statistical observation meaning that the parameters are estimated based on the observation of a limited set of data i.e. the model and its related parameters are not optimal, but the best possible.
5.4 Performance Index An important issue is the evaluation of the method's e ciency in defect identi cation as well as risk assessment. The e ciency is quantitatively presented by a performance index. The performance index (PI) is de ned as the percentage of the identi ed defects to the total identi ed clusters. Due to the statistical nature of the data, the analysis on identifying defective locations is prone to error.
Not always all captured clusters are defect and/or
all none captured clusters are not defect.
Therefore, it is necessary to perform
an evaluation of the tool based on its success or failure in forming a correct or incorrect clusters with high / low risk level. For this reason, a hypothesis of the captured cluster is defect is de ned. Likewise, the null-hypothesis is de ned as the cluster is not a defect, however, this could be correct or incorrect. The outcome of examining the null-hypothesis can be grouped in four types: 1. Rejecting a correct null-hypothesis - False Positive (FP): the cluster is not a defect but the algorithm labeled it as a defect. 2. Rejecting an incorrect null-hypothesis - True Positive (TP): the cluster is a defect and the algorithm labeled it as a defect. 3. Accepting a correct null-hypothesis - True Negative (TN): the cluster is not a defect and the algorithm labeled it as non-defect. 4. Accepting an incorrect null-hypothesis - False Negative (FN) : the cluster is a defect but algorithm fails to label it as defective. If the result of the test corresponds with what indeed is a real situation then the outcome is correct else an error occurred. Therefore, items 2 and 3 are indeed the correct outcome and the other two items are classi ed as error. Performance index is de ned based on the success and/or failure of the algorithm in capturing the defective/non-defective locations. Considering the itemized presented terms, the performance index is formulated as:
PI =
TP + TN FP + TP + TN + FN
(5.8)
In the next chapter, covering a number of interesting circuits, an overall overview of the success and/or failure is presented and the associated PI of the tool is calculated as part of the evaluation process.
Chapter 6
Evaluation of the automated defect detection algorithms The previous chapters explored approaches for knowledge based support of on-line PD diagnosis with defect identi cation and automated recognition. Examples provided in former chapters, illustrated the functionality of the developed methods. This chapter aims to validate their performance in assessing the condition of operational cable components. To establish a success rate, a selection of monitored cable circuits was subjected to the defect identi cation process. This chapter presents the results of applying it to datasets either taken under controlled measurement conditions or obtained on cable connections in operation.
6.1 Controlled measurement Controlled measurements are aimed to evaluate the tool under speci ed conditions.
For this reason two sets of measurements, namely controlled laboratory
measurement and controlled on-site measurement, were considered.
Laboratory measurement The basic idea was to perform a long term continuous measurement by applying both conventional PD measurement as well as on-line measurement by a single SCG unit. An arti cial defect in the insulation would be continuously monitored upon PD initiation and development over a long period of time. The initial test was set to stimulate corona discharges at a 4 m cable system through inserting a metal point at the end of the cable section. The SCG system needed to undergo some modi cations to function as a single sided sensor system. However, recording measurement data through SCG unit was adversely in uenced due to the very short length of cable for which the unit was never designed. At the same time the on-line measurements based on SCG in the eld had become operational (as from 2007). A large database comprising measurement results from various cable types, 79
CHAPTER 6.
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80
DETECTION ALGORITHMS
dierent cable accessories, ranging from a few hundred meters to over seven kilometers were becoming available. This provided the opportunity to study realistic eld data making the laboratory experiment super uous.
Therefore, this work
focuses on data from real cable connections instead of concentrating on laboratory measurements.
MiniNet measurement A few controlled measurements were still carried out in a small MV test grid at KEMA, The Netherlands, to monitor the degradation process of the insulation. The measurements were designed to study the PD initiation, its behavior during the aging process especially just before failure, and further on to apply the results for evaluation of the developed method. The set up consists of two main ring units, interconnected by a paper insulated lead covered (PILC) MV cable. The cable consists of two sections connected with an oil- lled joint.
Figure 6.1 shows the test set-up for experimental on-line PD
measurement at MiniNet.
Figure 6.1: MiniNet test set up for experimental investigation of on-line PD measurement under user de ned conditions [37]
6.1.
CONTROLLED MEASUREMENT
7000
81
(a) 7000 PD magnitude [pC]
PD magnitude [pC]
6000 5000 4000 3000 2000
6000
(b)
5000 4000 3000 2000 1000 0 420
1000 Time [min] 0 0
50
100
150 200 Location [m]
250
0
0
200 100 Location [m]
Figure 6.2: 2D (a) and 3D (b) PD mapping diagram obtained with the MiniNet set-up for an arti cially damaged joint at about 100 m.
Measurement I Data from one experimental run for about 7 hours, at the MiniNet was made. An electrode-bounded defect was embedded in the joint located at 96 m distance from one RMU. This type of defect which can generally be classi ed as internal discharge source, results in an asymmetric discharge pattern with regard to the sinusoidal voltage, where a large number of small discharges during the negative half cycle are followed by a smaller number of larger discharges at the positive half cycle [35]. The cable was energized with a 400 V / 10 kV transformer. During the measurement 397 PD records were stored in a database.
Each
record includes the data from the measurement over 20 ms (one power cycle) performed with intervals of one minute. Figure 6.2 shows the mapping diagrams for this measurement. The highly active region is located at about 100 m from the termination, corresponding to the joint location. PD charge density and occurrence rate patterns were created for this data set. As can be seen in Figure 6.3, PDs from the location around 50 m are averaged out in the density pattern (Figure 6.3a) and dropped close to zero in the occurrence rate pattern (6.3b). The distinct discharge locations remain prominently present for the patterns accumulated on an hourly basis. Since the duration of this measurement is relatively short, the patterns on a minute base patterns are analyzed instead to more clearly illustrate the data manipulation process by the developed defect detection algorithms. Figure 6.4 shows the PD density patterns and PD occurrence rate patterns created for the measurement dataset presented in Figure 6.3 before and after noise reduction. Discharges at the location around 50 m tend to disappear through the noise reduction algorithms. Discharges spread over the region between 200 m to 300 m are partly eliminated through the noise reduction tool as well. However, si-
CHAPTER 6.
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82
DETECTION ALGORITHMS
1600 7 (a)
7
1 0.12
(b)
500
0.1
300
0
50
100 150 200 Location [m]
250
300
0.08
Time [hr]
Time [hr]
400
0.06
200
0.04
100
0.02
0
0
50
100 150 200 Location [m]
250
300
0
Figure 6.3: PD charge patterns, a) charge density, b) charge occurrence rate accumulated on hourly basis obtained from the measurement with MiniNet set-up shown in Figure 6.2.
gni cant PD activity at this area remained unaected.PD activity from the location of the defect remains. Figures 6.4e and 6.4f show the result of the sequence clustering. The algorithm operates satisfactorily in detecting the defective location as well as the PD activity in the region between 200 m to 300 m which may point to a degrading cable section. A PD Cluster is also formed in density pattern around 50 m. As can be seen in the mapping diagram, there is a low intensity PD concentration around 50 m. This peak has not fully been removed during the noise reduction and its low concentration is also recovered by the sequence clustering algorithm. The pulse height distribution (PHD) patterns are applied for defect type identi cation. Figure 6.5a shows the PHD pattern for the discharges originating from the defective joint and Figure 6.5b depicts the logarithmic Weibull CDF tted to the data. The model ts the discharges from the defect well, except for low discharge levels. This is due to the starting point of the Weibull plot which poses our model to start from zero, while in the analysis we are restrained by the minimum
±
discharge magnitude which the PD measuring system can detect. the shape parameter estimated by the model 2.27
The value of
0.15 points to the presence of
internal discharges. Also, the PHD pattern applied to the discharges distributed
±
between 240 m to 250 m. Figure 6.6 shows the PHD pattern and Weibull t to the data. From the shape value of 2.24
0.10, it can be inferred that internal
discharges are occurring at this section. This is indeed realistic since the cable is a rather old PILC type cable, and it may already suer from degraded insulation.
6.1.
CONTROLLED MEASUREMENT
83
Figure 6.4: Application of the automated defect identi er to dataset from measurement at MiniNet - a) PD charge density pattern, b) PD charge occurrence rate, c) PD charge density pattern after noise reduction, d) PD charge occurrence rate after noise reduction, e) sequence clustering - PD charge density pattern, f ) sequence clustering - PD occurrence rate
CHAPTER 6.
EVALUATION OF THE AUTOMATED DEFECT
84
DETECTION ALGORITHMS 350
4 (b)
300
2
250
0
Logarithmic CDF
Pulse count
(a)
200 150 100
−2 −4 −6
50
Experimental data Maximum likelihood fit 95% Confidence interval
−8
0 0
1000
2000 3000 4000 5000 Discharge magnitude [pC]
6000
7000
−10 2
3
4 5 6 7 8 Logarithmic discharge magnitude
9
Figure 6.5: a) PHD pattern for PDs from defective joint and b) corresponding Weibull estimate for the defective joint
35
2
(a)
(b)
30 0 Logarithmic CDF
Pulse count
25 20 15 10
−2
−4
−6
experimental data Maximum likelihood fit 95% Confidence interval
5 0 0
500 1000 1500 Discharge magnitude [pC]
2000
−8 3
4 5 6 7 Logarithmic discharge magnitude
8
Figure 6.6: PHD pattern for PDs from defective joint and b) corresponding Weibull estimate - Cable insulation
Measurement II A second experiment, conducted with the same set-up, was arranged to create a moisture ingress in the main insulation. Moisture ingress is a common defect that occurs in PILC cables. The moisture penetrated into the cable degrades the insulation. In later stages PDs followed by electrical treeing and nal failure may occur. However, electrical treeing leading to breakdown may appear without any prior PD activity. The moisture ingress may occur if:
6.1.
CONTROLLED MEASUREMENT
85
Discharge magnitude [pC]
1200 1000 800 600 400 200 0 0 Figure 6.7:
50
100 150 200 Location [m]
250
300
PD mapping diagram - Measurement with forced water ingress at
about 100 m from the termination
If the cable end is not su ciently water tight before or during the installation. Mechanical forces transferred to the joint may cause joint displacement and therefore, provide a path for water to penetrate to the cable insulation. Such mechanical forces may originate from heavy cyclic loading in, for instance,
cables connected to a wind turbine park. Damages to the lead sheath caused for instance by tree root growth can consist of cracks / holes in the lead sheath that results in moisture ingress.
The lead sheath of a healthy PILC cable was intentionally damaged and the section was exposed to water penetration. As a result, it was expected that water stays between the insulation and the lead sheath and consequently would cause:
Decrease of the propagation velocity of injected pulses through the cable. PD occurrence, electrical treeing and electrical breakdown or only thermal breakdown.
However, stimulating a PD activity under controlled condition appeared to be harder than was anticipated. The path for water penetration into the cable was blocked by fat concentration from the cable oil.
Figure 6.7 shows the mapping
diagram for the PD recorded for this measurement.
No changes in PD activity
was observed during one week of measurement. In fact, it was learned from the controlled experiments that long term PD degradation resembling realistic defects, is not easy to stimulate. Either the degradation
CHAPTER 6.
EVALUATION OF THE AUTOMATED DEFECT
86
DETECTION ALGORITHMS
goes so fast or no degradation progresses at all. Therefore, it was decided to turn the focus on the real eld data where many kilometers of cable systems including all type of possible defects became available.
6.2 Application to eld data The smart cable guard (SCG) system has become operational and being widely used by Dutch utilities as well as by utilities abroad. This resulted in establishing an extraordinary unique database for PD measurements records continuously taken each minute, which enabled us to study a large number of circuits and assessing actual eld data including upcoming defects. The merits of analyzing on-line measured PDs is that they are caused by an actual aging/degradation process under operational conditions of the cable network. Operational conditions include thermal loading, environmental conditions such as humidity of the soil, etc. In addition, during on-line measurement, the PD behavior can be monitored even until ultimate failure. The failed section/ component can sometimes be made available for visual inspection to reveal the actual defect. This will support establishing the correlation between the measured PD and the type of the defect. At the moment of writing this thesis, over 20 circuits in which incidents have occurred have been studied in detail.
Three circuits are already
presented in Chapter 5 to illustrate the developed algorithms. a selection of ve interesting circuits is made.
For this section,
These circuits are discussed and
applied as test cases for the developed algorithms. The selection includes a large variety of cases in terms of cable type, length, presence of RMUs in the circuits, etc.
In section 6.3, a complete overview of the studied defects is given and the
performance of the tool to identify the PD concentrations is shown through performance index analysis.
Example I The rst example utilizes the measurement result for monitoring 247 m combined PILC/XLPE cable system. The system includes one resin joint, 4 polymer joints and 2 terminations.
PDs for this system, are monitored continuously over one
month. The PD mapping diagram for this circuit is presented in Figure 6.8. During monitoring, a sudden PD activity was detected at the transfer point between cable and resin joint located about 44 m away from one termination. 6.9 shows the PD related patterns created for this measurement.
Figure
Based on the
concentrated PD activity observed on the mapping diagram, occurring as well, as a distinct intense behavior in both charge density (6.9a) and occurrence rate (6.9b) patterns, the owner of the circuit was advised to take immediate action to avoid the breakdown. The cable section as well as the joint were replaced.
6.2.
APPLICATION TO FIELD DATA
87
Discharge magnitude [pC]
Discharge magnitude [pC]
1500
1000
500
0 0
50
100 150 Location [m]
200
1500 1000 01−Apr−2008
500 0 0
50
250
100
150
200
250 01−Mar−2008
Location [m]
Figure 6.8: PD mapping diagram for a 247 m PILC/XLPE combined cable circuit - Example I
Figure 6.9: PD patterns for the mapping diagrams given in Figure 6.8 - a) discharge density, b) discharge occurrence rate - Example I
Visual inspection revealed that the transition point between cable and joint was pressed and deformed due to the growth of tree root through the transfer point. Figure 6.10 shows the result of the visual inspection.
The areas with deformed
lead covering, which is separated from the common insulation of the three cores, are visible within the white circles in the gure. The data was subjected to the automated defect identi cation tool to validate its capability for capturing the defect location. Also the defect type identi cation by means of Weibull modeling is investigated. Figure 6.11 shows the application of the PD cluster detection tool to the cable dataset.
As can be seen in both
patterns, the clustering algorithm captured the intense PD activity at a location around 44 m.
CHAPTER 6. 88
EVALUATION OF THE AUTOMATED DEFECT DETECTION ALGORITHMS
Figure 6.10: Visual inspection for the damaged connection after replacement of the cable/joint - Example I
Two other clusters were detected at the location of the termination at 0 m and a joint located at about 100 m. Comparing the identi cation patterns with the PD mapping, discharge density and occurrence rate patterns, one can observe that there are ongoing discharge activities at both locations. In addition, there is cluster formed on the occurrence rate pattern at about 20 m. This discharging location originated possibly from random occurring discharges in PILC cable itself which are not fully eliminated during the pattern cluster detection process for the occurrence rate pattern.
Figure 6.11: Detected PD patterns clusters in - a) discharge density, b) discharge occurrence rate - Example I
6.2.
APPLICATION TO FIELD DATA
89
1
Risk level
0.8 0.6 0.4 0.2 0 01−Mar−2008 11−Mar−2008 21−Mar−2008 01−Apr−2008 Figure 6.12: Risk variation from defective joint at 44 m shown in Figure 6.8 Example I
Advise on remedial action is based on the risk assessment parameter. The risk level for the defective location reached the value close to 1 (see Figure 6.12) during the monitoring period, calling for immediate action, while the other two peaks showed risk variation of lower level (risk index varies around 0.03). These clusters are not calling for immediate action, however, further monitoring may be sensible to check whether the activity changes, indicating a developing defect. The cluster at 20 m appearing in the discharge occurrence pattern is presented with a risk level close to zero. The discharge source could be external disturbances or coming from the cable insulation itself which is not harmful for the cable insulation. The PD distribution from the location of the defect was further analyzed to investigate the defect type identi cation based on Weibull distribution. Figure 6.13, shows the result of the Weibull shape parameter for the identi cation of the defect. As can be observed at 6.13a, the shape parameter (β ) is mainly varying between 2 and 3, pointing to internal discharges which was con rmed after visual inspection of the joint. During the period just before its replacement, the
β
value decreased
to less than 2. This drop indicates a change in the defect. The scale parameter (α) shown in Figure 6.13b shows a growing amplitude followed by a decreasing level during the time just before the replacement, which may be interpreted as change of state and getting closer to failure. A PD level just before the breakdown, may decrease or even cease e.g. due to formation of a carbonized conductive path.
CHAPTER 6.
EVALUATION OF THE AUTOMATED DEFECT
90
DETECTION ALGORITHMS
Figure 6.13: Weibull model parameter based on PDs from the joint at 44 m in Figure 6.8, a) shape parameter (β ) , b) scale parameter (α) - Example I - The insets show the variation of the
β
and
α
parameter of PDs located at 44 m.
Example II Data were recorded for a 1801 m PILC cable system including 13 resin and oil lled joints, 4 termination and a RMU within the cable connection. In this circuit an oil- lled joint located between 993 m and 998 m showed intense PD activity. Figure 6.14 shows the mapping diagram for the period of a month when intense activity was observed. Based on this behavior, it was advised to replace the joint to avoid a failure. After the joint was replaced, the discharge activity ceased as can be seen in 6.14b .
2500 Discharge magnitude [pC]
Discharge magnitude [pC]
(a) 2000
1500
1000
500
0 0
500
1000 Location [m]
1500
2000
3000 (b) 2000 1000
0 01−Mar−2009 22−Feb−2009 15−Feb−2009 08−Feb−2009 01−Feb−2009 0
2000 1000 Location [m]
Figure 6.14: PD mapping diagram for 1801 m PILC cable with concentrated at an oil- lled joint - Example II
6.2.
APPLICATION TO FIELD DATA
91
The PD activity was further investigated. Figure 6.15 shows the charge density, occurrence rate patterns and their associated clustering. As clearly observed from Figure 6.15a and 6.15b, the peak present in the mapping diagrams also occurs as an intense discharging site in both density and occurrence rate patterns (though averaged over a period of one hour and normalized). Figure 6.15c and 6.15d show the application of the defect identi er applied to the density and occurrence rate patterns respectively. The peak was indexed as level 1 risk. Figure 6.16 shows the variation of risk at the location of defect. Weibull analysis was applied to the data from the location of the defect for classi cation of the defect type. Figure 6.17 shows the pattern for shape (β ) and scale (α) parameters of the model. The shape factor is close to 2. The component is an oil- lled joint and appeared to be extremely degraded.
The pattern for
the Weibull scale parameter shows a slight decreasing trend. This can again be understood from the formation of a conductive carbonized path. The component has undergone a visual inspection (Figure 6.18) where electrical tree formation was observed. The origin of the defect may have been the ingress of moisture in the compound.
Figure 6.15: PD patterns for the PILC cable Figure 6.14: a) charge density, b) occurrence rate; c) density and d) occurrence rate clustering - Example II
CHAPTER 6. 92
EVALUATION OF THE AUTOMATED DEFECT DETECTION ALGORITHMS
1
Risk level
0.8 0.6 0.4 0.2 0 01−Feb−2009
11−Feb−2009 21−Feb−200901−Mar−2009
Figure 6.16: Risk variation from defective oil- lled joint at 995 m shown in Figure 6.14 - Example II
Figure 6.17: Weibull distribution for PD magnitudes from the oil- lled joint at about 995 m in Figure 6.14, a) shape parameter (β ) and b) scale parameter (α) Example II. The insets show the time evolution of the Weibull parameters.
6.2.
APPLICATION TO FIELD DATA
93
Figure 6.18: Visual inspection for the damaged oil- lled joint after replacement: a) moisture in the joint, and b) traces of electrical tree structure were observed Example II
Example III Example III presents the result for a 5661 m cable circuit circuit of 20 kV XLPE cable network including 16 cold shrink joints, 8 terminations per cable core and 3 RMUs. This circuit is selected because it has several RMUs in the circuit which in uences the propagation of the PD pulses. The fact that PD activity occurred only for a couple of days makes it also interesting for further analysis. Figure 6.19 shows the mapping diagrams for this cable circuit. The joint located at about 4000 m was discharging for three consecutive days followed by a period of ceased PD activity. Although the circuit is quite noisy, the peaks of discharges are still visible despite the long cable length. Both PD charge density and occurrence rate patterns shown in Figure 6.20 revealed increased activity around the location of the joint while partly averaging out the random noise signals distributed along the cable. For XLPE type of power cable any observed PD level is not acceptable. The defect was detected in time and the joint was advised to be replaced. Figure 6.21 shows the outcome of the visual inspection which con rmed degradation which could be attributed to a hot connector.
The heat was dissipated by a badly
installed conductor connector which damaged the cold shrink joint. The data collected for this measurement, was subjected to further analysis to determine the correlation between defect and characteristics of the Weibull model parameters.
Figure 6.22 shows the Weibull shape parameter pattern for
PDs occurring during the three consecutive days of PD activity. The shape factor value varies between 2.0 and 2.5 at the location of the defect.
CHAPTER 6.
EVALUATION OF THE AUTOMATED DEFECT
94
DETECTION ALGORITHMS
3500 (a) (b) 2500
Discharge magnitude [pC]
Discharge magnitude [pC]
3000
Discharging location 2000 1500 1000 500 0 0
1000
2000 3000 Location [m]
4000
5000 5661
4000
Discharging location
3000 2000 1000 0 0
01−Jun−2009 1000
2000
3000
4000
Location [m]
5000 5661 01−May−2009
Figure 6.19: Discharge mapping diagram for a 5661 m XLPE cable. Temporal PD activity is observed at about 4000 m for three consecutive days - Example III
Figure 6.20: PD patterns for XLPE cable circuit with mapping diagram shown in Figure 6.17 : a) charge density, b) occurrence rate - Example III
6.2.
APPLICATION TO FIELD DATA
95
Figure 6.21: Visual inspection of a defective cold shrink joint - Example III
2.5
Estimated Beta parameter
2
1.5
1
0.5
Logarithmic CDF
Beta Parameter
2
0 −2 −4 −6 2
experimental data Maximum likelihood fit 95% Confidence interval 3 4 5 6 7 Logarithmic discharge magnitude
0 01−May−2009
01−Jun−2009 Date−Time
Figure 6.22: Weibull distribution, shape factor (β ) from defective cold shrink joint - Inset shows the estimated shape factor for a day of PD activity and its related 95% con dence interval - Example III
CHAPTER 6.
EVALUATION OF THE AUTOMATED DEFECT
96
DETECTION ALGORITHMS
Figure 6.23: PD patterns after noise reduction applied to the data shown Figure 6.17, a) charge density, b) occurrence rate - Example III
The performance of the automated defect identi er is shown in Figure 6.23. In the PD charge density and PD charge occurrence rate patterns after noise reduction, the noise distributed along the cable length is almost completely eliminated, whereas the discharge peaks remain distinctly visible in the patterns. Figure 6.24, shows the result for clustering for this circuit.
The sequence
clustering algorithm works well in capturing the PD peak even though the PD activity raised only for a very limited time and with magnitudes hardly exceeding the background.
The cluster is indexed as level 1 risk.
Figure 6.24b shows the
variation of the risk level at the spot of the identi ed defect.
1 (b)
Risk level
0.8 0.6 0.4 0.2 0 01−May−2009
11−May−2009
21−May−2009
01−Jun−2009
Figure 6.24: Defect identi cation tool applied to density pattern of Figure 6.21a: a) sequence clustering, b) risk variation from a location of defective joint at about 4000 m - Example III
6.2.
APPLICATION TO FIELD DATA
97
Example IV A 221 m PILC cable shows PD activity almost, as shown in Figure 6.25, over the entire cable length. This behavior can possibly occur due to mechanical stressed caused by load cycling.
The density pattern presented in Figure 6.26a, shows
the impact of distributed discharging activity. It is averaged out by normalizing and averaging the related PD quantities.
Based on the observed patterns, the
termination was suspected to be in a bad state.
Inspection of the termination
revealed a low oil level as probable cause. The data was used to investigate the functionality of sequence clustering applied to the PD density pattern. In Figure 6.26 three broad regions can be recognized in the charge density pattern (Figure 6.26a) which are clearly identi ed by the sequence clustering (Figure 6.26b). The risk index was calculated for all three defects resulting in a values close to 1. The low oil level in the termination was correctly identi ed by risk indexing. PD activities from two other locations, distributed over a broad region (Figure 6.26a), are considered less harmful and basically are caused by load cycling.
However,
during the noise reduction this region was narrowed resulting in a concentrated region for the cluster which consequently resulted in high risk index.
1600 (a)
1200 1000
Discharge magnitude [pC]
Discharge magnitude [pC]
1400
800 600 400 200 0 0
50
100 150 Location [m]
200
250
2000 1500 01−Jan−2010
1000 500 0 0
50
100
150
200
250
01−Dec−2009
Location [m]
Figure 6.25: PD mapping for a 221 m PILC cable connection with distributed PD activity - Example IV
CHAPTER 6.
EVALUATION OF THE AUTOMATED DEFECT
98
DETECTION ALGORITHMS
Figure 6.26: PD patterns for PILC cable connection - mapping diagram shown in Figure 6.23, a) PD charge density pattern , b) sequence clustering - Example IV
Figure 6.27 shows the Weibull distribution model applied on the PDs from the cable termination. The shape parameter (β ), at the location of the defect varies between 1.6 to 2.4. This implies the possible existence of either or both surface and internal discharge.
At the other two locations the shape parameter varies
between 2.0 and 4.0 pointing to internal discharges.
Figure 6.27: Weibull shape parameter (β ) pattern for the PILC cable shown in Figure 6.23. The insets shows the variation of IV
β over a period of a month - Example
6.3.
PERFORMANCE OF THE AUTOMATED DEFECT DETECTION
99
Example V A last interesting circuit is a 1663 m combined PILC/XLPE cable, including 17 joints, 2 grease termination, 2 polymer terminations and a RMU in the connection. PD visualization showed three intense active discharge peaks (Figure 6.28a). The analysis of the PD patterns indicated a high risk situation. The owner was advised to take immediate action. However, the failure occurred before maintenance took place.
The inspection, revealed that the cable at the PILC part was heavily
degraded (Figure 6.29). Figure 6.28b shows the PD density pattern for this circuit. The clustering algorithm managed to catch all discharging peaks (Figure 6.30a). Figure 6.30b, shows the variation of the risk index calculated for the defective site located at about 200 m. This defect cluster was assigned as high risk.
6.3 Performance of the automated defect detection The presented examples are only part of the cases investigated during this thesis work.
In total about 20 circuits were selected for further investigation.
In this
section the results of the investigated circuits, detected defects, identi ed state of the defects, and the related risk indices are discussed. Table 6.1 gives an overview of all the diagnosed circuits.
These circuits are
ordered based on circuit type. The rst three columns in Table 6.1 specify circuit number, type of the cable circuit and the related speci cation including the length
8000 Discharge magnitude [pC]
(a) 6000
4000
2000
0 0
200 400 600 800 1000 1200 1400 1600 Location [m]
Figure 6.28: PD related patterns for a 1663 m combined PILC/XLPE cable connection: a) mapping diagram, b) charge density - Example V
CHAPTER 6.
EVALUATION OF THE AUTOMATED DEFECT
100
DETECTION ALGORITHMS
Figure 6.29: Visual inspection of PILC segment for the discharging site of 170 m visible in Figure 6.26 - Example V
1
(b)
Risk level
0.8 0.6 0.4 0.2 0 15−Dec−2010
25−Dec−2010
05−Jan−2011
20−Jan−2011
Figure 6.30: Application of defect identi cation tool: a) sequence clustering on the pattern shown in 6.28b, b) risk variation at the location of defect at about 200 m from the termination - Example V
of the cable, type and number of the accessories in the connections. The fourth and fth columns give information on the defect site and whether the defect was detected by SCG system.
Column sixth shows whether a nal breakdown has
occurred and the last column explains the result of the inspection of the defective components.
Circuit nr. C1 Ex. 1 C2 (Circuit A) C3 Ex. 2 C4-1 C4-2 C5-1 C5-2 C6 C7 C8 C9 C10 (Ex. 4) C11-1 C11-2 C11-3 (Circuit C)
Circuit type PILC PILC PILC PILC PILC PILC PILC PILC PILC PILC XLPE -
Circuit speci cation 247 m, 5 joints, 2 termination 214 m, 1 joints, 2 termination 1801 m, 13 joints, 4 termination and 1RMU 666 m, 12 joints, 2 termination 666 m, 12 joints, 2 termination 665 m, 10 joints, 4 termination and 1 RMU 367 m, 2 joints, 2 terminations 2529 m, 23 joints, 2 terminations 1048 m, 8 joints, 6 termination and 2 RMUs 221 m, 4 joints, 2 terminations 7057 m, 19 joints, 2 terminations -
Defect site Joint at 44 m Joint at 139 m Joint at 995 m Cable at 396 m Cable at 386 m Cable at 418 m Joint at 310 m Joint at 128 m Termination at 0 m Joint at 1396 m Cable at 80 m Termination at 0 m Cable/Joint at 500 m Cable/Joint at 4000 m Cable/Joint at 5000 m -
Defect identi ed Yes Yes Yes Yes Yes Yes No No Yes No Yes Yes Yes Yes Yes -
Breakdown No No No Yes No Yes Yes Yes No Yes Yes No No No No -
Remarks on the defect Damaged transfer point between cable and joint due to the growth of the tree root in the vicinity Unknown Moisture ingress and Electrical treeing formation Moisture ingress - lead sheath was damaged root growing across the cable Moisture ingress - damaged lead sheath Unknown Unknown Low oil level Unknown Moisture ingress - lead sheath was damaged due to the tree root growing across the cable Low oil level Bad installation of the conductor connectors resulted in heat dissipation and hot connectors damaged the cable and the cold shrink joints
6.3. PERFORMANCE OF THE AUTOMATED DEFECT DETECTION
Table 6.1: Overview of the studied circuits
101
Circuit nr. C12 C13 C14 (Circuit B) C15 C16 C17 (Ex. 5)
Circuit type XLPE XLPE XLPE XLPE XLPE PILC + XLPE -
Circuit speci cation 6260 m, 16 joints, 6 terminations, 2 RMUs 4258 m, 12 joints, 2 terminations 5661 m, 16 joints, 8 terminations, 3 RMUs 5661 m, 16 joints, 8 terminations, 3 RMUs 6232 m, 18 joints, 4 terminations, 1 RMUs 1663 m, 17 joints, 4 terminations, 1 RMUs
Defect site Joint at 3343 m Joint at 1678 m Joint at 4022 m Joint at 3526 m Joint at 2600 m PILC cable at 170 m -
Defect identi ed Yes Yes Yes Yes Yes Yes -
Breakdown No Yes No No Yes Yes -
Remarks on the defect Bad installation of the conductor connectors resulted in hot connectors which damaged the joint Bad installation of the conductor connectors resulted in hot connectors which damaged the joint Bad installation of the conductor connectors resulted in hot connectors which damaged the joint Bad installation of the conductor connectors resulted in hot connectors which damaged the joint Bad installation of the conductor connectors resulted in hot connectors which damaged the joint PILC cable section was heavily degraded
102 CHAPTER 6. EVALUATION OF THE AUTOMATED DEFECT DETECTION ALGORITHMS
6.3.
PERFORMANCE OF THE AUTOMATED DEFECT DETECTION
103
From the circuits included in Table 6.1, about 86% of the circuits were diagnosed in time by monitoring the PD activity, out of which 28% led to failure due to late remedial action. For about 14% of the studied circuits no PDs were detected and cables failed in service, indicating the degradation stage could already be in the stage just before breakdown or the degradation is not associated with PD activity. Table 6.2, presents the application of the clustering tool to the cable circuits introduced in Table 6.1. An overview of the analyzed circuits is tabulated, according to False Positive, True Positive, True Negative, False Negative terminology introduced in Chapter 5. Table 6.2: Overview of the clustering tool applied to study cable circuits presented in Table 6.1
Circuit nr. C1 Risk Index C2 Risk Index C3 Risk Index C4-1 Risk Index C4-2 Risk Index C5-1 Risk Index C5-2 Risk Index C6 Risk Index C7 Risk Index C8 Risk Index C9 Risk Index C10 Risk Index C11-1 Risk Index C11-2 Risk Index C11-3 Risk Index
True positive (TP) Defect at joint RI = 1.00 Defect at joint RI = 1.00 Defect at joint RI = 1.00 Defect at cable RI = 0.99 Defect at termination RI = 1.00 Defect at termination RI = 1.00 Defect at cable RI = 0.99 Defect at cable RI = 0.99 Defect at cable RI = 0.99
False positive (FP) 2 non-defect clusters RI = 0.60 and 0.74 1 non-defect cluster RI = 0.98 1 non-defect cluster RI = 0.98 1 non-defect cluster RI = 0.91 2 non-defect clusters RI = 1.00 1 non-defect cluster RI = 0.99 -
True negative (TN) 2 non-defect clusters RI = 0.03 1 non-defect cluster RI = 0.03 1 non-defect cluster RI = 0.37 -
False negatives (FN) Defect at cable RI = 0.00 Defect at cable RI = 0.00 Defect at joint No PD from defect Defect at cable No PD from defect Defect at joint No PD from defect Defect at cable RI = 0.00 -
CHAPTER 6.
EVALUATION OF THE AUTOMATED DEFECT
104
DETECTION ALGORITHMS
Circuit nr. C12 Risk Index C13 Risk Index C14 Risk Index C15 Risk Index C16 Risk Index C17 Risk Index
True positive (TP) Defect at joint RI = 0.99 Defect at joint RI = 1.00 Defect at joint RI = 0.99 Defect at joint RI = 0.99 Defect at joint RI = 1.00 Defect at cable RI = 1.00
False positive (FP) 1 non-defect cluster RI = 0.59 1 non-defect cluster RI = 1.00 3 non-defect clusters RI = 1.00 2 non-defect clusters RI = 1.00
True negative (TN) 2 non-defect clusters RI = 0.03 9 non-defect clusters RI =0.03 - 0.37 -
False negatives (FN) -
As shown in Table 6.2, apart from the defects detected by the sequence clustering tool, other concentrated activities were also captured. For all the detected clusters which were identi ed as real defect a valid warning was given based on the estimated risk index.
For non defect clusters, either rejected due to a low
calculated risk level, or in some cases due to medium/high risk index, a warning was given. For the circuits C5-2, C6 and C8, where no PDs were observed, the risk index was not assigned. For circuits C4-1, C4-2 and C9, where actual defects were not identi ed by the clustering tool, the locations were indexed as zero risk in false negatives group. The e ciency of the the developed tool in detecting discharge peaks, identifying the state of the insulation, and quantitatively represent the state in form of risk index is assessed an presented in groups based on the (non)observed / (non)captured (non)defective clusters. The performance index given by Equation 5.8 in Chapter 5, is calculated based on the presented results. In total 30 warnings were given out of which 15 were valid. Besides, 15 valid rejection of clusters made. Taking into account the valid and invalid warnings / rejections made, and excluding the circuits for which no PDs was observed, the performance of the tool reaches about 63 %.
Further ne tuning by DNV KEMA performed after this
thesis work, showed that it was possible to get a total performance of 84 % for 32 actual defects and 5 very noisy circuits without any defect, identi ed until April 2011 (See Appendix B).
Chapter 7
Conclusion and Recommendation The main objective of the research described in this thesis is to develop an automated statistical partial discharge (PD) interpretation approach to be utilized as a decision support tool for the on-line PD measurement system (SCG system). Correct interpretation of PDs based on the statistical defect identi ers requires understanding of the deterioration mechanisms taking place in the insulation material. During the course of this work, common degradation mechanisms in MV cable system have been investigated. Most of the defects in the MV cable insulation lead to PD activity followed by formation of electrical trees proceeding the ultimate failure. Continuous measurement of PDs has proven to be a useful approach to minimize the risk of failure in the distribution network, since it provides means to quickly respond on sudden arising defects. The vast stream of data that needs to be processed and interpreted calls for an automated procedure for assessing the severity of the defect. The contributions made by this thesis for analyzing PD data from the SCG system are summarized in Section 7.1. Recommendations for future research are given in Section 7.2.
7.1 Conclusion Basic quantities addressing PD activity, are charge and number of PDs occurring at each location. These quantities form the basis for identifying the condition of the insulation. They are further manipulated to de ne derived quantities which represent the insulation condition more accurately. In this research, derived quantities were de ned as the total PD charge and the total PD number per power cycle and per unit of length. In addition, maximum values can be taken occurring during a certain duration and over a fraction of the cable length.
useful values for this length and duration have been investigated. studied over 1
Choices for Patterns are
of the cable length and in 1-hourly time blocks. Not always the
exact location of the components is known. Moreover, if for a speci c cable the propagation velocity is not accurately known, location errors may occur. This fact rules out the analysis per component, by focusing on priori chosen small range 105
106
CHAPTER 7.
CONCLUSION AND RECOMMENDATION
which encloses e.g. a cable joint. The overall location accuracy is about 1% due to systematic errors. On the other hand, the reproducibility of the defect location
can be much smaller, since the reproducibility is informative regarding how localized the defect is, a 1
fraction is preferred. The PD patterns were studied in
various time spans i.e. 1-hourly, 8-hourly and 24-hourly. For all detected defects (weak spots and failures), the patterns in various time blocks are analyzed. The comparison showed that all time spans are useful. Short periods i.e. 1-hourly patterns provides earlier possibility of warning and usually still contain su cient PDs to allow for statistical signi cance. Also PD activity related to load cycling can be recognized. Therefore, the hourly based data selection was considered appropriate for identifying the observed defects. The reader should realize that this statement is based on the present SCG characteristic of measuring PDs during one power cycle per minute, i.e. 0.03 % of the time. Would this change in the future, other time spans (shorter time spans) could become of interest as well. Recent literature indicated that the distribution of PDs is characteristic for the type of defect in a cable system. Various statistical distributions including Normal distribution, Gamma distribution and Weibull distribution have been studied. It is shown in this work, that the shape parameter of the Weibull model can help to identify the type of the defect i.e. distinguish between internal discharge, surface discharge or corona discharge.
A point of concern is the in uence of the PD
detection level on the distribution parameters.
This level is chosen for optimal
sensitivity for each cable circuit and therefore diers for dierent locations. This threshold may aect the distribution parameters. On-line diagnostics reveal behavior as it actually occurs in a cable in service and give access to trends in PD activity [4]. The addition of the PD trend with time is a major advantage of continuous monitoring above using o-line systems. Basically, the main information on MV insulation condition is obtained from the variation in time of all parameters rather than from their absolute values. From the experience at present, it has become clear that PDs in shrink joints of XLPE cable and water ingress in PILC cable require immediate attention [85]. Variation in parameters points toward changes in the status of the defect and therefore assisting in forecasting upcoming faults. Approaches for automated defect detection have been investigated. PD activity as a function of length and time occurs in almost any imaginable distribution and repetition. This complicates methods for recognition of speci c patterns. Two approaches have been focused on. The rst one involves recognizing a PD location by tting a normal distribution. The second one is based on nding clusters of PD activity both in place and in time. The normal distribution t is time consuming compared to sequence clustering. Although both tools function in detecting the discharge peaks, it is decided to utilize the sequence clustering for its e cient implementation. Defects which result in local PD activity are easily recognized also if they only occur intermittently or during a short period of time.
PD activity
from a broad region is recognized as well by clustering method and sometimes, if
7.2.
RECOMMENDATIONS
107
this activity is intense, a high risk index is assigned. However, for PILC cable this high distributed PD activity is not considered to be problematic and the high risk index should be overruled. Complete automated rejection of this behavior would still be premature, since it should be 100% guaranteed then that similar behavior always can be ignored. Risk indexing is presented to quantitatively express the state of the insulation. The risk index is calculated based on the probability of occurrence for each preliminary risk factor, namely total discharge density per cluster, total occurrence rate per cluster and the cluster width. The logistic distribution is used to estimate the probability of the above mentioned factor. The probability models are constructed based on the available PD datasets. The e ciency has been investigated with regard to its success in capturing defective / non-defective clusters and assigning them with high/low risk index and present them as performance index. For the studied cases given in this work the performance index of about 63% was achieved. Basically, at the starting point of this project, in 2001, the utilities and KEMA, sponsoring this project were hoping for reaching minimum of 50% performance index for human made interpretations and identi cations. Therefore, obtaining this performance index for the automated identi cation system can be considered as successful at the time of nalizing this work.
However, further ne tuning of the approach performed by KEMA and
obtaining a performance index of 84% (see Appendix B), emphasizes that the approach was indeed successful.
7.2 Recommendations Despite the success rate, eort should be put in further improving the performance. The following issues could be addressed in future research.
Not all types of defects give rise to PD activity. For instance, water treeing only results in PD when it converts to electrical treeing, which is already the end of the life for the component.
Changes in dielectric properties of
the insulation can be an indication of the aging and the state of the aging. Incorporating other diagnostic techniques which e.g. probes the water ingress
would enhance the functionality of the diagnostic tool. In this research, several insulation state indicators, basic and derived quantities, are explored. Further measures that can be used to indicate quantitatively the state of the insulation are, for instance, discharge energy, quadratic rate, etc. It is recommended to investigate those quantitative measures to express the state of the cable insulation after having gathered data from
su cient defects for a statistical signi cant analysis. A two-parameter Weibull distribution was used for identifying the defect type. A weaknesses of this model is that it is only capable of identifying a
108
CHAPTER 7.
CONCLUSION AND RECOMMENDATION
single discharge source. In practice, there is a possibility of multiple discharge sources at the single defective site especially when the cable is reaching its end of life. Higher order Weibull distribution, 5-parameter Weibull, to be further
investigated to realize how this model can assist in defect type identi cation. Trends in Weibull distribution parameters can be indicative of the change of insulation state. However, still a lot of practical information is required to show its eectiveness of pointing to speci c defect types. In many conventional PD pattern recognition technique studies, the focus is to analyze the distribution of the phase angle at which the PDs occur in order to identify the defect type. However, in [86], it is noted that for solid or liquid insulation, the phase angle of the external voltage source is not a meaningful parameter of the PD process.
Basically, the PDs are initiated
by the enhanced local electric eld at the site of the defect and not by the absolute value or phase angle of the externally applied voltage. According to [86] the time interval between the pulse occurrence or voltage dierence are more meaningful values since consecutive discharges do not occur randomly. It is recommended to study the patterns of consecutive discharges for defect
type identi cation. The proposed models for the probability of occurrence used for risk analysis are constructed based on the statistical PD database which grows over time. It is recommended to re-assess the models in later stages. In addition, measures incorporated in risk analysis can be extended to include more defect representative factors such as Weibull model parameters. Moreover, to improve the risk indexing algorithm, an uncertainty analysis can be included. Besides the risk index itself, an estimate of the expected reliability of the risk can be given.
Similar methods are already applied for evaluation of
transformer insulation [87].
Appendix A
Weibull Model The two-parameter Weibull distribution is a widely applied distribution in eld of partial discharge pattern recognition. The pulse height distribution of the PDs can be approximated by the Weibull model. The density function of this distribution is given in (A.1).
β qi β−1 qi β f (qi ; α, β) = · exp − α α α where
qi
is the apparent charge and
α
and
β
(A.1)
are the scale and shape parameter,
respectively. The cumulative distribution function of Weibull is a more convenient one to model the observed data:
qi β F (qi ; α, β) = 1 − exp − α
(A.2)
To estimate the model parameters, several techniques such as Least Squares (LS) and the Maximum Likelihood (ML) are available for estimating the parameters. Choosing appropriate criteria to estimate the parameters in the model is of fundamental importance. For instance, if we deal with a linear model, then applying the least square may appear more convenient and less time consuming as compared to the maximum likelihood.
However, many models are intrinsically nonlinear.
Including an error estimate in the model is important to express the reliability of the model. This error term not always distributed normally. Therefore using the least square to estimate the parameters is not appropriate. In such cases employing the Maximum Likelihood results in more reliable and accurate estimates. In the following section, the maximum likelihood estimation of the parameters of the Weibull distribution is presented. Afterwards, the sensitivity of the model to the uncertainty of the parameters is studied, i.e. appropriate margins of con dence for the maximum likelihood estimates (MLE). 109
110
APPENDIX A.
WEIBULL MODEL
A.1 Maximum Likelihood Estimates (MLE): One of the approaches for estimation of unknown parameters is the maximum Likelihood method. This technique picks the value of the model parameters that make the data more likely than any other values of the parameters that would make them.
Recalling the Weibull density function for partial discharge height
distribution from previous section, we calculate the likelihood function as follows.
f (qi ; α, β) =
qi β β qi β−1 · exp − α α α
(A.3)
Assuming that the data are independent and identically distributed (each has the same probability distribution as the others and all are mutually independent), the likelihood function is a product of
n
univariate densities:
f (q1 , q2 , . . . , qn ; α, β) =
n Y
f (qi ; α, β)
(A.4)
i=1 The likelihood function with Weibull density can be represented by:
β−1
Q n
n q i=1 i β L= αn α
"
n β X qi · exp − α i=1
# (A.5)
We can take the logarithm of the above function in order to change the product to the summation. Therefore:
n # P qβ n i X Λ = ln (L) = −n · β · ln (α) + n · ln (β) + (β − 1) ln (qi ) − i=1β α i=1
"
To nd these parameter
α
and
β,
(A.6)
we can maximize the likelihood function over
both parameters simultaneously i.e. setting the derivatives of the likelihood function with respect to
∂Λ ∂α ∂Λ ∂β
α
and
β
to zero.
=0 =0
Therefore:
n ·β α
−n · ln (α) +
n β
+
n P
−β ·α
ln (qi ) + α
−β
n P qiβ = 0
ln (α) ·
i=1
Rewriting A.7. a results in the estimate of
n P
qiβ i=1
α:
−α
−β
n P
(A.7)
ln (qi ) ·
i=1
qiβ
=0
A.2.
CONFIDENCE INTERVAL
111
P n
qiβ
e
1e β
i=1 α e= n
(A.8)
Substituting the above result in the second equation will result in:
n
n X n· + ln (qi ) − βe
n e P qiβ · ln (qi ) =0 n e P qiβ
(A.9)
By numerically solving Equation A.9, the value of the shape parameter is determined. In this work the method of Newton-Raphson has been used as a numerical method to nd the estimate
β.
A.2 Con dence interval Due to the inherent statistical uctuations due to the number of the samples [88], there will be uncertainties in the estimated parameters. One of the approaches to indicate the uncertainty of estimation is to present the con dence interval with a high con dence level. In our work, with Con dence interval (CI) we refer to the bounds that represent the plausible values for the estimated parameters. Con dence interval can be calculated based on the Fisher Matrix. Con dence limits for Weibull parameters can be calculated as follow [79].
αU = α e · exp αL =
ka ·
e β √
βL = exp
ka
√
var(e α) α e
α e√ ka · var(α) e exp α e
βU = βe · exp
where
ka ·
ka ·
q
e) var(β e β
e) var(β
!
(A.10)
!
e β
is the inverse of the standard normal probability density function and
can be estimated by applying:
a = 1 − φ (ka ) a= with
δ
1−δ 2 that represent the con dence level.
The variance and covariance of the shape and scale parameters can be estimated by inverting the Fisher matrix:
112
APPENDIX A.
" ∂Λ var βe cov α e, βe − ∂β 2 = − ∂α∂Λ∂β cov α e, βe var (e α)
WEIBULL MODEL
− ∂α∂Λ∂β ∂Λ − ∂α 2
#−1 (A.11)
Appendix B
Overview of the results obtained by SCG system
The SCG has become operational since 2007. Over 200 units are installed in the eld to continuously monitor the cable network condition. The data are collected and interpreted by a defect identi cation tool developed further based on the ideas explained in this thesis. several circuits including 5 very noisy circuits was studied by the tool, where 32 critical locations were identi ed and in time notice was given by KEMA to the network owner. Table B.1 presents the circuit information and Table B.2 gives an overview of the obtained results. The obtained performance index for the current tool, excluding the contribution of the circuits where no PD was detected at all, is 84%. 113
Circuit nr. C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 -
Circuit type PILC PILC PILC PILC PILC PILC PILC PILC PILC PILC XLPE XLPE XLPE XLPE -
Circuit speci cation 247 m, 5 joints, 2 terminations 214 m, 1 joints, 2 terminations 1801 m, 13 joints, 4 terminations and 1RMU 666 m, 12 joints, 2 terminations 666 m, 12 joints, 2 terminations 665 m, 10 joints, 4 termination and 1RMU 367 m, 2 joints, 2 terminations 2529 m, 23 joints, 2 terminations 1048 m, 8 joints, 6 terminations and 2 RMUs 221 m, 4 joints, 2 terminations 7057 m, 18 joints, 2 terminations 6260 m, 17 joints, 6 terminations and 2 RMUs 4258 m, 12 joints, 2 terminations 5661 m, 16 joints, 8 terminations, 3 RMUs
Defect identi ed Yes Yes Yes Yes Yes Yes No No Yes No Yes Yes Yes Yes Yes Yes Yes -
Breakdown No No No Yes Yes Yes Yes No Yes Yes No No No Yes No -
Remarks on the defect Damaged transfer point between cable and joint was due to the growth of the tree root in the vicinity Unknown / Joint failed under laboratory test Moisture ingress and Electrical treeing formation Moisture ingress - lead sheath was damaged due to the tree root growing across the cable Moisture ingress - lead sheath was damaged Unknown Unknown Low oil level Unknown Moisture ingress - lead sheath was damaged due to the tree root growing across the cable Dry termination / Low oil level Bad installation of the conductor connectors resulted in heat dissipation and hot connectors Bad installation of the conductor connectors resulted in heat dissipation and hot connectors Bad installation of the conductor connectors resulted in hot connectors which damaged the joint Bad installation of the conductor connectors resulted in hot connectors which damaged the joint
114 APPENDIX B. OVERVIEW OF THE RESULTS OBTAINED BY SCG SYSTEM
Table B.1: Overview of circuits
Circuit nr. C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26 C27 C28 -
Circuit type XLPE XLPE PILC+XLPE PILC+XLPE PILC PILC XLPE PILC PILC PILC PILC PILC PILC XLPE -
Circuit speci cation 5661 m, 16 joints, 8 terminations, 3 RMUs 6232 m, 18 joints, 4 terminations, 1 RMUs 1663 m, 17 joints, 4 terminations, 1 RMU 1579 m, 8 joints, 2 terminations 1667 m, 12 joints, 2 terminations 2140 m, 23 joints, 8 terminations, 3 RMUs 6020 m, 18 joints, 6 terminations, 2 RMUs 1181 m, 16 joints, 12 terminations, 5 RMUs 394 m, 4 joints, 2 terminations 729 m, 5 joints, 2 terminations 475 m, 4 joints, 2 terminations 708 m, 6 joints, 2 terminations 474 m, 9 joints, 6 terminations, 2 RMUs 7108 m, 19 joints, 2 terminations
Defect identi ed Yes Yes Yes Yes Yes Yes Yes Yes Yes -
Breakdown No Yes Yes No No Yes Yes No -
Remarks on the defect Bad installation of the conductor connectors resulted in hot connectors which damaged the joint Bad installation of the conductor connectors resulted in hot connectors which damaged the joint Heavy discharging along the cable Damaged joint + Moisture ingress Damaged joint Moisture ingress and Electrical treeing formation Low oil level in joint Bad installation of the conductor connectors resulted in hot connectors which damaged the joint Bad installation of the conductor connectors resulted in hot connectors which damaged the joint Damaged termination -
115
Circuit type XLPE PILC+XLPE PILC+XLPE PILC XLPE PILC -
Circuit nr.
C29 C30 C31 C32 C33 C34 -
7555 m, 26 joints, 8 terminations, 3 RMUs 1098 m, 13 joints, 10 terminations, 4 RMUs 547 m, 6 joints, 6 terminations, 2 RMUs 591 m, 10 joints, 2 terminations 900 m, 8 joints, 4 terminations, 2 RMUs 1528 m, 12 joints, 2 terminations
Circuit speci cation Yes Yes Yes Yes Yes Yes Yes -
Defect identi ed No No No No No No -
Breakdown Damaged joint Damaged termination Damaged joint Damaged joint -
Remarks on the defect
116 APPENDIX B. OVERVIEW OF THE RESULTS OBTAINED BY SCG SYSTEM
117
Table B.2: Overview of circuits analyzed by the defect identi cation tool used by KEMA
Circuit nr. C1 Risk Index C2 Risk Index C3 Risk Index C4, C5 Risk Index C6 Risk Index C7 Risk Index C8 Risk Index C9 Risk Index C10 Risk Index C11 Risk Index C12 Risk Index C13 Risk Index C14 Risk Index C15 Risk Index C16 Risk Index C17 Risk Index C18 Risk Index
True positive Defect at joint RI = 1.00 Defect at joint RI = 1.00 Defect at joint RI = 1.00 Defect at cable RI =1.00 Defect at cable RI =1.00 Defect at cable RI =1.00 Defect at joint RI = 1.00 3 Defects at cable RI =1.00 Defect at cable RI =0.97 Defect at cable RI =1.00 Defect at cable RI =0.99 Defect at cable RI =1.00 Defect at cable RI =1.00 3 defects at cable RI =1 Defect at cable RI = 1.00
False positive 3 non defect clusters RI =1.00 1 non defect cluster RI = 1.00 1 non defect at cable RI = 1.00 -
True negative 2 non defect cluster RI = 0.00 1 non defect cluster RI = 0.01 -
False negatives Defect at cable No PD from defect Defect at joint No PD from defect -
APPENDIX B.
OVERVIEW OF THE RESULTS OBTAINED BY SCG
118
Circuit nr. C19 Risk Index C20 Risk Index C21 Risk Index C22 Risk Index C23 Risk Index C24 Risk Index C25 Risk Index C26 Risk Index C27 Risk Index C28 Risk Index C29 Risk Index C30 Risk Index C31 Risk Index C32 Risk Index C33 Risk Index C34 Risk Index
SYSTEM
True positive Defect at joint RI = 1.00 Defect at joint RI = 1.00 Defect at cable RI = 0.97 Defect at cable RI = 1.00 Defect at cable RI = 0.82 2 defects at cable RI = 0.77, 0.86 Defect at cable RI = 1.00 Defect at cable RI = 1.00 Defect at joint RI = 1.00 Defect at joint RI = 1.00 Defect at joint RI = 1.00 Defect at cable RI = 1.00
False positive 1 non defect at cable RI = 1.00 2 non defect at cable RI = 1.00 1 non defect cluster RI = 0.99 1 non defect cluster RI = 1.00
True negative 5 non defect clusters RI = 0 ~ 0.45 1 non defect cluster RI = 0.59 7 non defect clusters RI = 0 ~ 0.08 1 non defect cluster RI = 0.00 1 non defect cluster RI = 0.57 2 non defect clusters RI = 0.00, 0.01 2 non defect clusters RI = 0.00, 0.01 -
False negatives -
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List of abbreviations and symbols [1]
127
List of abbreviations and symbols α
scale factor for the Weibull distribution
αL
lower con dence level for Weibull scale parameter
αU
upper con dence level for Weibull scale parameter
β
shape factor for the Weibull distribution
βL
lower con dence level for Weibull shape parameter
βU
upper con dence level for Weibull shape parameter
R
real number
δ(0)
Dirac pulse
a
local capacitance for loss less cable model
b
capacitance of the dielectric parallel to defect
c
capacitance of the defect
F(z)
logistic function representing the probability of occurrence
Fdens
probability of occurrence for PD charge density representing identi ed clusters
fi
estimated value by peak identi er for the PD related values
Focc.rate
probability of occurrence for PD occurrence rate representing identi ed clusters
Ftrend−dens
probability of occurrence for trend in charge density for identi ed clusters
Fwidth
probability of occurrence for certain width for identi ed clusters
IP D
PD current in frequency domain
iP D
PD signal 129
130
LIST OF ABBREVIATIONS AND SYMBOLS
k
shape factor for the Gamma distribution
ka
inverse of the standard normal probability density function
L
likelihood function for Weibull distribution
l
local inductance for loss less cable model
lef f
eective length
ni,j
number of PD within selected length and time
PDdens
PD charge density
PDmax.dens
maximum PD charge density
PDmax.occ.rate
maximum PD occurrence rate
PDocc.rate
PD occurrence rate
Q1
rst quartile (25th percentile)
Q3
third quartile (75th percentile)
Qapp
induced PD charge
qi,j
discharge magnitude within selected length and time
Qsens
charge contained by detected signal
RI
risk index
r
regression coe cient for logistic function
R2
goodness of t measure
Rpre
pre-de ned value for goodness of t measure
T
current transmission coe cient
Tef f
eective measuring time
V
Voltage of the discharge pulse
yi
PD related value provided to peak identi er
Z0
characteristic impedance for loss-less cable
ZC
characteristic impedance
Zload
total RMU impedance
Zsens
ideal constant transfer
LIST OF ABBREVIATIONS AND SYMBOLS
131
Γ
Gamma distribution
γ
propagation coe cient
Λ
logarithmic likelihood function for Weibull distribution
µ
mean value for the Normal distribution
σ
variance for the Normal distribution representing the width
θ
scale factor for the Gamma distribution
α b
scale parameter for Weibull distribution estimated by MLE method
βb
shape parameter for Weibull distribution estimated by MLE method
ybi
mean PD related value provided to peak identi er algorithm
AC
Alternating Current
CBM
Condition Based Maintenance
CDF
Cumulative Density Function
CI
Con dence Interval
CM
Corrective Maintenance
CU
Controller Unit
DC
Direct Current
DG
Distributed Generation
FAT
Factory Acceptance Test
FN
False Negative
FP
False Positive
FT
Fractional Time
HV
High Voltage
IEC
International Electrotechnical Commission
IQR
Interquartile Range
LAW
Location Averaging Window
LV
Low Voltage
MLE
Maximum Likelihood Estimates
132
LIST OF ABBREVIATIONS AND SYMBOLS
MV
Medium Voltage
OWTS
Oscillating Wave voltage Test System
PD
Partial Discharge
PD-OL
Partial Discharge Monitoring On-line with Location
PHD
Pulse Height Distribution
PI
Performance Index
PILC
Paper-Insulated Lead-Covered
PM
Preventive Maintenance
RCM
Reliability Centered Maintenance
RMU
Ring Main Unit
SAT
Site Acceptance Test
SCG
Smart Cable Guard
SIU
Sensor/Injector Unit
SMA
Simple Moving Average
TBM
Time Based Maintenance
TDR
Time Domain Re ectometry
TN
True Negative
TP
True Positive
TSO
Transmission System Operator
VLF
Very Loe Frequency
XLPE
Cross-Linked Polyethylene
List of Publication [1]
S. Mousavi Gargari, P.A.A.F. Wouters, P.C.M.J. van der Wielen and E.F. Steennis: Partial discharge parameters to evaluate the insulation condition of on-line located defects in medium voltage cable networks , IEEE Transactions on Dielectrics and Electrical Insulation, vol.18, no.3, 2011, pp.868-877.
[2]
S. Mousavi Gargari, P.A.A.F. Wouters, P.C.J.M. van der Wielen and E.F. Steennis:
Statistical approach to identify the discharge source in MV ca-
bles and accessories , International Conference on Condition Monitoring and Diagnosis, Beijing, April 2008, 4p. [3]
S.
Mousavi
Gargari,
P.A.A.F.
Wouters,
P.C.J.M.
van
der
Wielen
and
E.F.Steennis: Statistical analysis of partial discharge patterns and knowledge extraction in MV cable systems , 10th International Conference on Probabilistic Methods Applied to Power Systems, Mayagüez, May 2008, 5p. [4]
S. Mousavi, P.A.A.F. Wouters, P.C.M.J. van der Wielen and E.F. Steennis: Experiences with on-line PD measurements and interpretation for MV cable systems eld data analysis , Nordic Insulation Symposium, Gothenburg, June 2009, 4p.
[5]
Peter A.A.F. Wouters,
Shima Mousavi Gargari,
Paul Wagenaars,
Peter
C.J.M. van der Wielen, E. Fred Steennis: Practical experiences and technical challenges in large scale introduction of on-line PD diagnosis for power cables , 9th International Conference on Properties and Applications of Dielectric Materials, Harbin, vol.3, July 2009, pp.976-979. [6]
S. Mousavi, P.A.A.F. Wouters, P.C.M.J. van der Wielen and E.F. Steennis: Continuous condition monitoring of MV cable and PD interpretation , Proceedings of the 16th International Symposium on High Voltage Engineering, Cape Town, August 2009, 6p.
[7]
P.A.A.F. Wouters,
S. Mousavi Gargari,
P. Wagenaars,
I.J. Tigchelaar,
B.H.M.M. Simons, P.C.J.M. van der Wielen, E.F. Steennis: Technical developments and practical experience in large scale introduction of on-line PD diagnosis , Proceedings of the 16th International Symposium on High Voltage Engineering, Cape Town, August 2009, 6p. 133
134
[8]
LIST OF PUBLICATION
Shima Mousavi Gargari, Peter A.A.F. Wouters, Peter C.J.M. van der Wielen, E. Fred Steennis:
Practical experiences with on-line PD monitoring and
interpretation for MV cable systems , 10th IEEE International Conference on Solid Dielectrics, Potsdam, July 2010, pp.434-437. [9]
P.A.A.F. Wouters, Y. Li, S. Mousavi Gargari, P. Wagenaars, E.F. Steennis: Apparent charge magnitude in on-line PD diagnostics on medium-voltage power cables , 17th International Symposium on High Voltage Engineering, Hannover, August 2011, 6p.
Acknowledgement [1]
It is impossible to make a step forward in life without guidance, support, advice, help, kindness and the extended love by others, which was also applicable to me. Making this manuscript would have not been possible without the support and the help of several individuals who in one way or another contributed and extended their valuable assistance in the preparation and completion of this study. First and foremost, I would like to oer my sincerest gratitude to my professor and promoter, Fred Steennis, without whom I would have not been standing where I am standing today. Everybody may know how keen, sharp, smart, hard working, and knowledgeable person he is, but not that much people may know how kind, caring, supportive, patient and dedicated person he is.
One could never learn
about such characteristics of him, unless working closely with him, which I had the honor. Behind that serious face, an esteemed unsel sh soft-hearted down-toearth caring person is hidden. For me he has been always an inspiration. I looked up to him and I learned a lot from him, not only for my work but also for my life. Without him passing through all the obstacles that existed on the path of this research would have been for sure impossible. His invaluable advice at the right time made this work as well as my life much easier as what they could have been for me. Here comes the famous name in our EES group, Peter Wouters, my daily supervisor and co-promoter that I simply lack word to thank him. According to me, Peter is a very special person, very knowledgeable and most importantly very down-to-earth person.
I rarely have seen such a human being in my life, and
working with him was such a blessing I received in my life. He is truly a caring person, doesn't matter how busy he is, he always nd time for people who ask for his hands. I would go to his o ce with a problem and I would get back to my o ce with the solution. His valuable knowledge and insight into this research was the source of inspiration and solutions for me. He is the guy who knows everything about everything. Peter, I never forget what you have done for me during these years, especially the last December, when I was dealing with nalizing the draft of this manuscript. What you have done for me, spending all the weekends, Christmas night, New year Eve in the cold o ce of CR 1.16 to review, to comment and to make sure I will be in time to submit my thesis is not a thing that goes away 135
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ACKNOWLEDGEMENT
from my mind and my heart and I will remember it to the end of my life. I am so grateful for every single thing you have done for me through the last 6 years. Next, I would like to acknowledge dr.
Peter van der Wielen for sharing his
knowledge and ideas with me during all those meetings that we had on monthly basis.
He is a brilliant person and tough to convince.
It has been always a
challenge for me to come up with quali ed explanation that he accepts, but I always enjoyed all those fruitful discussions.
Peter thanks so much for all the
discussion and guidance during the years of working together. I would also like to appreciate my doctoral committee members, prof. Slootweg, prof.
Johan Smit, prof.
Math Bollen, and ir.
Han
Maarten van Riet for
allocating their precious time to review my thesis and providing me with valuable comments that helped improving the presented manuscript. I would also like to take the opportunity to express my heartfelt appreciation to prof. Jan Blom, former chairman of the EES group. His wisdom in managing the group made our group a pleasant place to be. His initiative in creating Ph.D. student activities was such a brilliant measure to integrate the non-Dutch and Dutch students together. the students in his group.
He indeed cared a lot about both work and life of During those so-called 3-monthly meeting, the rst
thing was to make sure the life is good and the work is still joyful, and then he would check the progress of the work. Jan, your positive kind caring attitude was something which really added a lot to the atmosphere of our EES group. I started during the presidency of Jan Blom, and I nished this chapter of my life during the presidency of prof. Wil Kling. Wil is a very hard working and direct to point person. He really has made our EES ship to go forward. If he realizes something is good and important, then he makes his mind for it regardless of how busier he would become. Though being so busy, he tried to continue the old tradition of EES group in keeping track of his students' lives. He also continued with supporting the PhD team activities, and even let us to go beyond the borders and have a trip to London.
Wil, I am thankful to you for supporting us to enjoy the somehow
tough boring Ph.D. student life. During the course of this thesis, I was blessed by having the best possible colleagues one may wish for. Paul Wagenaars and Arjan van Schijndel have played important roles in my working life. Sharing the so-called CR 2.13 with them was de nitely a pleasure. All those supposedly constructive work related discussions, chit chats, and above all, their helps when I was struggling with MATLAB, was something that made my life much easier to live.
Guys, I really treasure your
presence in my life and all the supports you extended to me. I also like to thank my fellow Ph.D. student colleagues (too many to be mentioned) and other colleagues from EES group for providing a pleasant atmosphere to spend a majority of my daily life in. Besides, I had the honor to work with colleagues at PDOL group at DNV KEMA. Asido Patar Nainggolan, Ad Kerstens, Andre Cuppen and Edwin Maurer, many thanks to all of you, for all the support and assistance during the past years. The research work presented in this dissertation would have not been possible without the nancial and technical supports from the industry.
I would like to
ACKNOWLEDGEMENT
137
thank DNV KEMA, Alliander, Enexis and Stedin for providing me the opportunity and the facility to do this research work. During the last stage of my work, I started working at K.C.I engineering. It was tough to work and study at the same time, especially when I was nalizing my research work. I like to thank my colleagues at K.C.I. especially those within electrical engineering department for supporting me through this stage.
Joop
Keizer, is the one whom I am utmost grateful to. His cautious kind caring attitude was de nitely a key which helped me a lot to nalize this work. Taking over my responsibilities when I needed to leave to nish my thesis, is not a thing that everybody would do, especially with a pile of work he had for himself. Joop, I am so grateful to you for all your kindness, support, encouragement and above all for all the things that I am learning from you on daily basis. I look over the journey past and I see many people without their love and support throughout these years, it would be tough to nish the path I started. On personal level, I would like to thank some of those people. Not that I forgot the rest, but the list is too long to be tted in this acknowledgment. So, hoping for their understandings, I take the right to only specify some of them. My dearest Sogol Golchin, a friend forever is at the top. Besides sharing the friendship and our sweet Apartment 11, we shared happiness, sadness, ups and downs of each other's life.
We grew up together and we are growing old together.
We shared
an important decade of our life together and I learned a lot from you, thanks for being part of my life and thanks for being the sister whom I never had before you. Next, comes the turn for the caring supportive sister-friend of mine Bahar Jami, who extended me her support and love over approx. 7000 km distance, from Iran to the Netherlands. Bahar, thank you for every single thing that you have done for me since the start of our everlasting friendship and I am so happy to have you. Nasrin Ravandoust, a lady with a golden heart and a great soul comes the next. Maman Nasrin , I am so grateful for all the kindness and love that I have received from you since the moment I met you the rst. Susana Rodrigues, a lady whose kindness I received a lot during the last years. Susana, I am truly indebted to you for all your concern and consideration about me and my work and I do respect you with all my heart. Yan Guifen, many thanks for your patience and understanding when Peter was working on my thesis during the time he should have been home instead. I also would like to thank my friends Nima Farkhondeh, Lusine Hakobyan, Maryam and Marjan Sarab, Petr Kadaruk, Bibiana Cortes, Totis Karaliolios, Jerom de Haan, Ioannis Lampropoulos, Yajing Chai, Reza Mousavi and Families Golchin, Geers, Ghamarian, Ghosi, Jami, Keizer, Khalili, Molaie, Hosseini, Talaie, Shiri, and my dearest relatives (my dearest grandmother, uncles, aunts and cousins) for their friendships, supports and loves. Last but not least comes my dearest family, my parents and my two brothers, the ones who give meaning to every single minute that I live.
Hamidreza and
Alireza, the heroes of my life (de nitely after my father), are the kindest, sweetest and loveliest brothers one could wish for. All my memories and present moments have been colored by your presence. Every time I look back, I realize that I've got
138
ACKNOWLEDGEMENT
a treasure in my life and I feel so grateful to the creator. Guys, thanks for being the shoulders I can lean on. My adorable parents, I would not have contemplated this road if not for them. I just cannot describe my feelings about them. I cannot make any sentence to properly portrait their roles in my life.
There is nothing
that I can make and dedicate to them that can even partially shows have grateful I am to them with all my heart and my soul. Maman va Baba , I wish for a day that I can return a very small part of what you have done for me during all the years of my existence.
Curriculum Vitae [1] Shima Mousavi Gargari was born on 21-04-1981 in Tehran, Iran. After nishing her high school in 1998, she studied Electrical Engineering at Azad University of Tehran in Tehran, Iran.
After receiving her Bachelor of Science
(B.Sc.) degree in electronics in 2003, she joined Poolad Consulting Engineers Co., Tehran, Iran, where she worked as an instrumentation engineer.
In 2004, she
started her Master of Science (M.Sc.) studies in Electric Power Engineering at the Royal Institute of Technology (KTH), Stockholm, Sweden. In 2006, she graduated within the RCAM group on the subject of Reliability analysis of Reliability Assessment of Complex Power. From 2006 she started a PhD project at Eindhoven University of Technology (TU/e) at Eindhoven, the Netherlands, of which the results are presented in this dissertation. Since 2011 she is employed at K.C.I engineering rm.
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