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Implementation of Low Cost Non-DFT based Phasor Measurement Unit for 50 Hz Power System Talha Ahmed Bhatti, Abdur Raheem, Tanveer Alam, Muhammad Owais Malik, Abdullah Munir Department of Electrical Engineering NED University of Engineering and Technology Karachi, Islamic Republic of Pakistan [email protected] Abstract- The interconnection of several small & large systems has increased the probability of large blackouts in the power grid. One of the leading reason behind these blackouts is low reporting rate of existing devices and unavailability of synchronized system wide data. Synchrophasor technology provides solution to the problem. Phasor Measurement Unit (PMU) is the most front end device required to implement Synchrophasor Technology. The paper proposes a non-DFT based technique for development of PMU conformed to IEEE standards C37.118.1 and C37.118.2. The proposed technique aims to reduce the processing time & sampling rate which ultimately leads to reduced cost. The hardware of PMU is implemented using Raspberry pi Model B+, ADS 1015 and GPS (Global Positioning System) model PA6H on MTK3339 chipset

extracted from the data in real time using statistical analysis tools. Furthermore control commands are generated depending whether the estimated parameters are within prescribed range or not. The data & information flow of synchrophasor network is depicted in Fig. I.

~

GPSSatellite

~ ~ Data analyze

Make decision Control system

Keywords - IEEE C37.118, Phasor Measurement Unit, PMU, Synchrophasor

T

I. INTRODUCTION

power system of Pakistan is facing severe blackouts which causes great economic and life losses. The main reason behind these blackouts is the unavailability of system wide synchronized data and high latency of the monitoring system. When there is a fault in one part of system or if any major generating unit is cut off from the national grid, then the remaining generators also begin to trip one by one due to excessive load stresses. This catastrophic failure results in a system wide blackout. The blackouts can be eliminated by making the monitoring system quick and wide using synchrophasor smart grid technology. For example if any line becomes faulty or generation reduces due to any fault, the smart control station isolates the faulty section and reduces the load in proportion with the generation within no time and thereby avoiding the system wide load stresses. Synchrophasor Smart Grid technology can contribute to overcome several currently prevailing problems of power system e.g. blackouts, low reporting rate, unavailability of universally synchronized and time stamped data. The technology aims to improve existing supervisory control and data acquisition system (SCADA). The prime differentiator between conventional and smart grid is the use of synchrophasor data. The synchrophasor architecture consists of a network of PMUs whose output data is concentrated using Phasor Data Concentrator (PDC). There can be several stages of data concentration i.e. local, regional then super PDC. The concentrated data is then transmitted to monitoring & control center. Then several important electrical parameters are HE

978-1-5090-1252-7/16/$3l.00 ©2016 IEEE

Fig. I Synchrophasor infonnation flow architecture

II. PHASOR M EASUREMENT U NIT IMPLEMENTATION

The heart of phasor measurement unit is the measurement of time stamped phasors synchronized to a common time source called Synchrophasors. [I] Synchrophasor can be estimated using different algorithms on the sampled data. The choice of algorithm depends primarily upon three factors 1) Accuracy 2) Processing complexity (clock cycles required) 3) Response time. [2] The existing algorithms include the DFT (Discrete Fourier Transform) & its improved version FFT (Fast Fourier Transform) [3], weighted least square estimation [4] and discrete wavelet transform methods [5] . The methods require high sampling rates to avoid aliasing. It has been observed that many of these references are prone to the spectral leakage & interference phenomenon. Although several techniques are used to mitigate the effect but it further increases the overall processing complexity of the algorithm [6]. The IEEE standard C37.IIS.1 does not require calculation of higher order harmonics of the given signal i.e. only 1st harmonic or fundamental component is required which can be calculated using DFT based algorithm. Nyquist criteria will suggest sampling rate of 100 Hz or above for 50 Hz nominal frequency system, but in order to avoid spectral leakage that can occur due to the non-integer frequencies (non-multiple of 50 Hz) present in the signal a high sampling rate will be

required along with large window size. But large window will increase the latency i.e. response time & also the computational requirement of the algorithm. Therefore, fabrication of Non-DFT based PMU is represented in this research paper. The advantage of using this non-DFT based phasor estimation technique is that the required sampling rate of PMU is very low as compared to DFT & other techniques. Required sampling rate is equal to the desired reporting rate using this technique i.e. for 10 Hz reporting rate, the sampl ing rate is 10 sps (samples per second) & for 50 Hz, it is 50 samples per second & so on. Hence, there is no need to use high speed ADCs. Considering 10 Hz rate, the processing power of the microprocessor also reduces significantly because now only 10 samples are required to be processed. Hence, a very low processing power microprocessor and low cost ADC is required.

III. FUNCTIONAL BLOCKS OF PMU The major functional blocks ofPMU are depicted in Fig. 2: Sa tellites '--_+I

Time Synchronization 10 Hz SIgnal

Signal Acqui sitio n & Co nditi oning

UTCTime

From Cl ienL

,-----,

Data Processing

Data Commu nication

ToClienL

Fig. 2 Functional Diagram of PMU The signal conditioning block conditions both the input voltage and current signals. The supply voltage is reduced to suitable value using power resistors' VDR (Voltage Divide Rule). The current sensing is done using ACS 712 IC (Integrated Circuit). Its output is clamped at 2.5 V so a capacitor is connected after IC's output to remove this dc offset. Then these voltage & current phasors are sent to both bridge rectifier and hysteresis comparator circuits for further signal conditioning. Higher order harmonics are filtered from these signals using low pass filter as shown in Fig. 3. As it can be seen in Fig. 3 that the low pass filter also introduces a phase shift in the waveform but this phase shift is constant. Therefore, this additional phase shift is subtracted from each estimated phasor to obtain actual phase angle. t

,

t

I

I

time synchronization unit, is implemented on microcontroller PIC (Peripheral Interface Controller) 16f877a. The Linux is not a RTOS (Real Time Operating System), so it cannot be used for precise timing operations. Therefore, the sampling operation and transmission of message frames is dictated by the ADPLL's output. The ADPLL's output can be a 10, 50 Hz or any other higher frequency digital signal. The ADPLL's frequency is set as per the required reporting rate ofPMU. The ADPLL's output wave is synchronized with 1 PPS signal, so that the sampling and transmission operations are in synchronism with the 1 PPS signal. GPS is a part of time synchronization block. Two type of outputs are taken in the PMU development technique discussed in this paper, one is 1 PPS signal & other is UTC (Co-ordinated Universal Time) time. UTC time is sent to the Raspberry Pi using UART (universally asynchronous receiver/transmitter). The standard defmition of UTC is: "The time of day at the Earth's prime meridian (0° longitude)". This UTC time is converted into SOC (Second of Century) for time stamping the phasors conformed to IEEE standard. IV. MAGNITUDE ESTIMATION The fundamental component of signal is to be calculated only. The rms value of the given signal is estimated using ac to dc rectification. The ac signal is passed through a bridge rectifier to convert it into dc. The dc signal is then a reflection of the given ac waveform. The ac value is obtained by multiplying the output of rectifier by a pre-calculated factor Therefore, the magnitude of a signal can be measured by simply measuring the dc value using ADC. The magnitude measurement will not be effected by any frequency change, i.e. the magnitude measurement accuracy remains almost same at nominal and off nominal frequencies. V. PHASE ANGLE ESTIMATION The phase angle is estimated by calculating the time interval between the rising edge of the 1 PPS (Pulse Per Second) signal from GPS & the time of rising of sine wave at zero crossing shown in Fig. 4. But the phasor defmition pertinent to C37.11S.l defmes the phase angle of a cosine function so 90° is subtracted after phase angle calculation using the sine function .

,

~ \\!LJ~ ~\)J Figure 3 Harmonics removal using Low pass filter

All Digital Phase Lock Loop (ADPLL) which is present in

Fig. 4. Phase angle estimation

The sinusoidal input waveform is fIrst converted into square function using hysteresis comparator, so that it can be fed directly to the microprocessor. The output of comparator is a square wave having low & high voltage corresponding to the dual power supply voltages. The microprocessor Raspberry pi can only accept positive voltages, hence the negative portion of the square wave is clipped using a silicon diode. Then the positive portion is brought down to lower voltage i.e. 3.3 V using VDR. This positively clamped square wave can now be given to microprocessor, where the rising edge of the square wave reflects the zero crossing of sine wave. The microprocessor is programmed such that the rising edge generates an interrupt, the time of occurrence of this zero crossing (rising edge) is measured in the Interrupt service routine (ISR) & stored in a global variable. In a similar fashion, the occurrence time of rising edge of 1 PPS signal is measured and stored in another variable. The two variables containing the SOC (Second of Century) time are now subtracted to get the time interval between the PPS & sine wave zero crossing. This time is then converted into phase angle "8 " using (5). For 50 Hz the waveform there are 18000° in 1 sec, hence the time is multiplied by 18000 to convert time in seconds into angle as shown in (1) (1) 8 = t * 18000 Where, t= time between rising edge of 1 PPS signal & power signal waveform Then the angle in (1) is subtracted by 90 because cosine function is to be considered instead of sine as per standard. 8 = (t * 18000) - 90 (2) Now for further cycles, 360° is to be subtracted as each cycle represents 360°. Therefore, remainder operation is applied on (2) to get (3) (3) 8 = ((t * 18000) - 90) mod 360 Where, mod= remainder operation Finally (4) is obtained by subtracting 180 from (3) because according to the IEEE standard the angle can vary from -180 to + 180°, then the degrees are converted into radians. 8 = (((B- A+0.005)x 18000) mod 360) - 180)x(n/180)

(4)

Where, B=SOC time of zero crossing of sine wave A=SOC time of 1 PPS signal ' s rising edge In (4), B-A represents the time duration between the IPPS and the signal' s zero crossing.

f =B

1

(5)

Where, B= SOC time of zero crossing of current cycle B _old=SOC time of zero crossing of preceding cycle

:..

Tp

I-~~--, _

---

~I

_1- _ 1----,,...-- - , ____ ~-

I

B Fig. 5. Frequency Estimation

VII. RATE OF CHANGE OF FREQUENCY (ROC OF) CALCULATION Another quantity which is still required to be calculated before fIlling & transmitting the PMU output message frame, is the ROCOF (Rate of change of frequency). It is more prone to the amount of noise present in the input as compared to other phasor estimates. The rate of change of angle IS frequency, this relation is used to obtain rate of change of frequency from rate of change of angle as shown in (6): 0(t)=fO)(t)dt = 00 + ~O)t + 0.5 0)'t 2

(6)

Where Ts=Time of each sample Change of frequency (COF) O)'=ROCOF ~O)=

Writing (3) in matrix form to get (4)

00 01 O2 03

VI. FREQUENCY ESTIMATION The frequency estimation can be done on the basis of time obtained through phase estimation technique as shown in Fig. 5. The two consecutive rising edges ' occurrence time are subtracted to get the time period. Actually the time interval between the two rising edges is the time between zero crossings of the sine wave, so just taking the inverse of this time gives the frequency of the signal. The fundamental frequency is calculated using (5)

1

- B D ld = 1'5

1 1 1 1

0 Ts 2Ts 3Ts

[:~l

X cu' 2

0N - 1 (7)

Where 00=Estimated phase angle of fIrst sample 01 =Estimated phase angle of second sample 0 N_l=Estimated phase angle of Nth sample Let N =50 then Ts=0.02 sec Replacing the matrices in (7) with respective variables

yields (8) [0]=[B] [A]

(8)

Where, [0] Represents matrix of phase angle estimates in one complete window [B) Represents coefficient matrix of (8) [A] Represents unknown column matrix of (8) The Weighted Least-Squares (WLS) technique is used to calculate the unknown vector [A]. Equation (9) is implemented on PMU, in which the matrix G's value remains constant, whereas matrix 0 is updated containing the latest estimated phase angles. (9) is derived from (8), whereas (10) is used to calculate value of matrix "G". (9)

Where, [G)

=

[BTB]-IBT

(10)

The matrix [G) is stored in real time for use and it is pre calculated. It consists of 50 rows (for 50 sps system) and 3 columns. The multiplication of matrix [G) with [0] is performed to obtain matrix [A] from which ROCOF can be found at any time using (11). [3] ROCOF

w'

=-

21

View more...
extracted from the data in real time using statistical analysis tools. Furthermore control commands are generated depending whether the estimated parameters are within prescribed range or not. The data & information flow of synchrophasor network is depicted in Fig. I.

~

GPSSatellite

~ ~ Data analyze

Make decision Control system

Keywords - IEEE C37.118, Phasor Measurement Unit, PMU, Synchrophasor

T

I. INTRODUCTION

power system of Pakistan is facing severe blackouts which causes great economic and life losses. The main reason behind these blackouts is the unavailability of system wide synchronized data and high latency of the monitoring system. When there is a fault in one part of system or if any major generating unit is cut off from the national grid, then the remaining generators also begin to trip one by one due to excessive load stresses. This catastrophic failure results in a system wide blackout. The blackouts can be eliminated by making the monitoring system quick and wide using synchrophasor smart grid technology. For example if any line becomes faulty or generation reduces due to any fault, the smart control station isolates the faulty section and reduces the load in proportion with the generation within no time and thereby avoiding the system wide load stresses. Synchrophasor Smart Grid technology can contribute to overcome several currently prevailing problems of power system e.g. blackouts, low reporting rate, unavailability of universally synchronized and time stamped data. The technology aims to improve existing supervisory control and data acquisition system (SCADA). The prime differentiator between conventional and smart grid is the use of synchrophasor data. The synchrophasor architecture consists of a network of PMUs whose output data is concentrated using Phasor Data Concentrator (PDC). There can be several stages of data concentration i.e. local, regional then super PDC. The concentrated data is then transmitted to monitoring & control center. Then several important electrical parameters are HE

978-1-5090-1252-7/16/$3l.00 ©2016 IEEE

Fig. I Synchrophasor infonnation flow architecture

II. PHASOR M EASUREMENT U NIT IMPLEMENTATION

The heart of phasor measurement unit is the measurement of time stamped phasors synchronized to a common time source called Synchrophasors. [I] Synchrophasor can be estimated using different algorithms on the sampled data. The choice of algorithm depends primarily upon three factors 1) Accuracy 2) Processing complexity (clock cycles required) 3) Response time. [2] The existing algorithms include the DFT (Discrete Fourier Transform) & its improved version FFT (Fast Fourier Transform) [3], weighted least square estimation [4] and discrete wavelet transform methods [5] . The methods require high sampling rates to avoid aliasing. It has been observed that many of these references are prone to the spectral leakage & interference phenomenon. Although several techniques are used to mitigate the effect but it further increases the overall processing complexity of the algorithm [6]. The IEEE standard C37.IIS.1 does not require calculation of higher order harmonics of the given signal i.e. only 1st harmonic or fundamental component is required which can be calculated using DFT based algorithm. Nyquist criteria will suggest sampling rate of 100 Hz or above for 50 Hz nominal frequency system, but in order to avoid spectral leakage that can occur due to the non-integer frequencies (non-multiple of 50 Hz) present in the signal a high sampling rate will be

required along with large window size. But large window will increase the latency i.e. response time & also the computational requirement of the algorithm. Therefore, fabrication of Non-DFT based PMU is represented in this research paper. The advantage of using this non-DFT based phasor estimation technique is that the required sampling rate of PMU is very low as compared to DFT & other techniques. Required sampling rate is equal to the desired reporting rate using this technique i.e. for 10 Hz reporting rate, the sampl ing rate is 10 sps (samples per second) & for 50 Hz, it is 50 samples per second & so on. Hence, there is no need to use high speed ADCs. Considering 10 Hz rate, the processing power of the microprocessor also reduces significantly because now only 10 samples are required to be processed. Hence, a very low processing power microprocessor and low cost ADC is required.

III. FUNCTIONAL BLOCKS OF PMU The major functional blocks ofPMU are depicted in Fig. 2: Sa tellites '--_+I

Time Synchronization 10 Hz SIgnal

Signal Acqui sitio n & Co nditi oning

UTCTime

From Cl ienL

,-----,

Data Processing

Data Commu nication

ToClienL

Fig. 2 Functional Diagram of PMU The signal conditioning block conditions both the input voltage and current signals. The supply voltage is reduced to suitable value using power resistors' VDR (Voltage Divide Rule). The current sensing is done using ACS 712 IC (Integrated Circuit). Its output is clamped at 2.5 V so a capacitor is connected after IC's output to remove this dc offset. Then these voltage & current phasors are sent to both bridge rectifier and hysteresis comparator circuits for further signal conditioning. Higher order harmonics are filtered from these signals using low pass filter as shown in Fig. 3. As it can be seen in Fig. 3 that the low pass filter also introduces a phase shift in the waveform but this phase shift is constant. Therefore, this additional phase shift is subtracted from each estimated phasor to obtain actual phase angle. t

,

t

I

I

time synchronization unit, is implemented on microcontroller PIC (Peripheral Interface Controller) 16f877a. The Linux is not a RTOS (Real Time Operating System), so it cannot be used for precise timing operations. Therefore, the sampling operation and transmission of message frames is dictated by the ADPLL's output. The ADPLL's output can be a 10, 50 Hz or any other higher frequency digital signal. The ADPLL's frequency is set as per the required reporting rate ofPMU. The ADPLL's output wave is synchronized with 1 PPS signal, so that the sampling and transmission operations are in synchronism with the 1 PPS signal. GPS is a part of time synchronization block. Two type of outputs are taken in the PMU development technique discussed in this paper, one is 1 PPS signal & other is UTC (Co-ordinated Universal Time) time. UTC time is sent to the Raspberry Pi using UART (universally asynchronous receiver/transmitter). The standard defmition of UTC is: "The time of day at the Earth's prime meridian (0° longitude)". This UTC time is converted into SOC (Second of Century) for time stamping the phasors conformed to IEEE standard. IV. MAGNITUDE ESTIMATION The fundamental component of signal is to be calculated only. The rms value of the given signal is estimated using ac to dc rectification. The ac signal is passed through a bridge rectifier to convert it into dc. The dc signal is then a reflection of the given ac waveform. The ac value is obtained by multiplying the output of rectifier by a pre-calculated factor Therefore, the magnitude of a signal can be measured by simply measuring the dc value using ADC. The magnitude measurement will not be effected by any frequency change, i.e. the magnitude measurement accuracy remains almost same at nominal and off nominal frequencies. V. PHASE ANGLE ESTIMATION The phase angle is estimated by calculating the time interval between the rising edge of the 1 PPS (Pulse Per Second) signal from GPS & the time of rising of sine wave at zero crossing shown in Fig. 4. But the phasor defmition pertinent to C37.11S.l defmes the phase angle of a cosine function so 90° is subtracted after phase angle calculation using the sine function .

,

~ \\!LJ~ ~\)J Figure 3 Harmonics removal using Low pass filter

All Digital Phase Lock Loop (ADPLL) which is present in

Fig. 4. Phase angle estimation

The sinusoidal input waveform is fIrst converted into square function using hysteresis comparator, so that it can be fed directly to the microprocessor. The output of comparator is a square wave having low & high voltage corresponding to the dual power supply voltages. The microprocessor Raspberry pi can only accept positive voltages, hence the negative portion of the square wave is clipped using a silicon diode. Then the positive portion is brought down to lower voltage i.e. 3.3 V using VDR. This positively clamped square wave can now be given to microprocessor, where the rising edge of the square wave reflects the zero crossing of sine wave. The microprocessor is programmed such that the rising edge generates an interrupt, the time of occurrence of this zero crossing (rising edge) is measured in the Interrupt service routine (ISR) & stored in a global variable. In a similar fashion, the occurrence time of rising edge of 1 PPS signal is measured and stored in another variable. The two variables containing the SOC (Second of Century) time are now subtracted to get the time interval between the PPS & sine wave zero crossing. This time is then converted into phase angle "8 " using (5). For 50 Hz the waveform there are 18000° in 1 sec, hence the time is multiplied by 18000 to convert time in seconds into angle as shown in (1) (1) 8 = t * 18000 Where, t= time between rising edge of 1 PPS signal & power signal waveform Then the angle in (1) is subtracted by 90 because cosine function is to be considered instead of sine as per standard. 8 = (t * 18000) - 90 (2) Now for further cycles, 360° is to be subtracted as each cycle represents 360°. Therefore, remainder operation is applied on (2) to get (3) (3) 8 = ((t * 18000) - 90) mod 360 Where, mod= remainder operation Finally (4) is obtained by subtracting 180 from (3) because according to the IEEE standard the angle can vary from -180 to + 180°, then the degrees are converted into radians. 8 = (((B- A+0.005)x 18000) mod 360) - 180)x(n/180)

(4)

Where, B=SOC time of zero crossing of sine wave A=SOC time of 1 PPS signal ' s rising edge In (4), B-A represents the time duration between the IPPS and the signal' s zero crossing.

f =B

1

(5)

Where, B= SOC time of zero crossing of current cycle B _old=SOC time of zero crossing of preceding cycle

:..

Tp

I-~~--, _

---

~I

_1- _ 1----,,...-- - , ____ ~-

I

B Fig. 5. Frequency Estimation

VII. RATE OF CHANGE OF FREQUENCY (ROC OF) CALCULATION Another quantity which is still required to be calculated before fIlling & transmitting the PMU output message frame, is the ROCOF (Rate of change of frequency). It is more prone to the amount of noise present in the input as compared to other phasor estimates. The rate of change of angle IS frequency, this relation is used to obtain rate of change of frequency from rate of change of angle as shown in (6): 0(t)=fO)(t)dt = 00 + ~O)t + 0.5 0)'t 2

(6)

Where Ts=Time of each sample Change of frequency (COF) O)'=ROCOF ~O)=

Writing (3) in matrix form to get (4)

00 01 O2 03

VI. FREQUENCY ESTIMATION The frequency estimation can be done on the basis of time obtained through phase estimation technique as shown in Fig. 5. The two consecutive rising edges ' occurrence time are subtracted to get the time period. Actually the time interval between the two rising edges is the time between zero crossings of the sine wave, so just taking the inverse of this time gives the frequency of the signal. The fundamental frequency is calculated using (5)

1

- B D ld = 1'5

1 1 1 1

0 Ts 2Ts 3Ts

[:~l

X cu' 2

0N - 1 (7)

Where 00=Estimated phase angle of fIrst sample 01 =Estimated phase angle of second sample 0 N_l=Estimated phase angle of Nth sample Let N =50 then Ts=0.02 sec Replacing the matrices in (7) with respective variables

yields (8) [0]=[B] [A]

(8)

Where, [0] Represents matrix of phase angle estimates in one complete window [B) Represents coefficient matrix of (8) [A] Represents unknown column matrix of (8) The Weighted Least-Squares (WLS) technique is used to calculate the unknown vector [A]. Equation (9) is implemented on PMU, in which the matrix G's value remains constant, whereas matrix 0 is updated containing the latest estimated phase angles. (9) is derived from (8), whereas (10) is used to calculate value of matrix "G". (9)

Where, [G)

=

[BTB]-IBT

(10)

The matrix [G) is stored in real time for use and it is pre calculated. It consists of 50 rows (for 50 sps system) and 3 columns. The multiplication of matrix [G) with [0] is performed to obtain matrix [A] from which ROCOF can be found at any time using (11). [3] ROCOF

w'

=-

21

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