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Energy 109 (2016) 294e309
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Comparative studies on control systems for a two-blade variablespeed wind turbine with a speed exclusion zone Jian Yang a, Dongran Song a, b, Mi Dong a, *, Sifan Chen b, Libing Zou b, Josep M. Guerrero c a
School of Information Science and Engineering, Central South University, Changsha, PR China China Ming Yang Wind Power Group Co., Ltd., Zhongshan, PR China c Department of Energy Technology, Aalborg University, Denmark b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 2 November 2015 Received in revised form 17 March 2016 Accepted 24 April 2016
To avoid the coincidence between the tower nature frequency and rotational excitation frequency, a SEZ (speed exclusion zone) must be built for a two-blade wind turbine with a full rated converter. According to the literature, two methods of SEZ-crossing could be adopted. However, none of them have been studied in industrial applications, and their performance remains unclear. Moreover, strategies on power regulation operation are not covered. To fully investigate them, this paper develops two control systems for a two-blade WT (wind turbines) with a SEZ. Because control systems play vital roles in determining the performance of the WT, this paper focuses on comparative studies on their operation strategies and performance. In these strategies, optimal designs are introduced to improve existing SEZ algorithms. Moreover, to perform power regulation outside the SEZ, two operation modes are divided in the proposed down power regulation solutions. The developed control systems’ performance is confirmed by simulations and field tests. Two control systems present similar capabilities of power production and SEZ-bridging. Nevertheless, at the cost of significantly increased tower loads, one captures 1% more energy than the other. Overall consideration must be made for the control system selection for a WT with a SEZ. © 2016 Elsevier Ltd. All rights reserved.
Keywords: Two-blade variable speed wind turbine Control system Speed exclusion zone Tower resonance Power capture Tower loads
1. Introduction A wind turbine system is a system that converts mechanical energy obtained from wind into electrical energy through a generator. It can be categorized by types of generators used, power control methods, constant- or variable-speed operations, and methods of interconnection with the grid [1]. To ensure high performance while minimizing costs, new solutions are developed constantly for WT (wind turbines) (). Fundamental changes have been addressed, such as continuously variable transmissions [2,3] and new sensing technologies [4,5]. Meanwhile, advanced control algorithms have been widely studied, such as soft computing techniques [6,7] and sustainable control [8]. Despite the development of good concepts in recent years, engineering and science challenges still exist. Modern high power WTs are typically designed in a variablespeed type, capturing wind energy and reducing the mechanic
* Corresponding author. E-mail address:
[email protected] (M. Dong). http://dx.doi.org/10.1016/j.energy.2016.04.106 0360-5442/© 2016 Elsevier Ltd. All rights reserved.
loads effectively. However, a wide speed operation region allows the resonance between rotor rotary frequency and natural frequencies of other structural components. To tackle underlying problems, some methodologies are applied during the design phase, including natural characteristic calculations and potential resonance analyses [9]. Considerations include not only the certain gap reserved among the natural frequencies of the blades, tower and driver train but also the avoidance of coincidences among natural frequencies and external resonance force [10]. It is recommended that the eigen-frequency of the rotor blade be outside a 12% range of the rotational frequency of the WT and the lowest mode frequency of the tower be kept outside ranges defined as ±10% of the rotor frequency and ±10% of the blade passing frequency [11]. In practical applications, the tower resonance is dangerous because it results in the vibration of the whole WT set. For a three-blade WT, it is possible to move the natural frequency to the region between 1 P and 3 P by redesigning the tower's thickness and radius. However, this approach does not work for a two-blade WT because changing the tower's natural frequency to be lower than 1 P or higher than 2 P will greatly increase the cost. Therefore, to prevent the WT from operating in the SEZ (speed
J. Yang et al. / Energy 109 (2016) 294e309
Nomenclature
qset,qm wA,wB,wC, wD wb,wc wo wr p wr pl , wr ph wr t wr tl ,wr th wr m Topt Pset
the pitch angle set-point and the measured pitch angle. four speed points at optimal tip speed section. the lower and upper speed boundaries of the speed exclusion zone. the critical speed of a two-blade wind turbine. the speed reference of the pitch controller wr p in low power mode and high power mode the speed reference of the PI torque controller wr t in low power mode and high power mode the measured rotor speed the optimal generator torque the power command from wind farm controller
exclusion zone), the only feasible way is to redesign the control system. Control algorithms for a WT with a SEZ are described in previous works [12e17]. Among these works, two control approaches can be distinguished. The first one, recorded in [12], is based on the torque control with a conventional lookup table. The second one, proposed in [13e15], is developed based on a proportional integral (PI) torque control method. In both of them, a certain speed region, including the critical speed and its vicinity, is built up to form the SEZ. Differences between them are the means of establishing and bridging over the SEZ. The first approach is to create an ambiguous function between rotor speed and generator torque, so that the generator can accelerate to cross the SEZ, through an unbalanced relation between the aerodynamic torque and demanded generator torque. The second is to gradually adjust the speed reference from one fixed speed boundary to another. Despite the two approaches available, studies about their applications in real wind turbines are few. As far as we know, only in [16] are different widths of SEZs, based on the second approach, investigated and validated on a 1.3 kW test rig. In addition, in [17], the first approach is employed for the design of a two-bladed WT's control system. In the wind energy industry, control strategy validation through field trials is vital and irreplaceable. Based on field trials and related data analysis, for the control approach applied in [17], two drawbacks are exposed: i) the experimental turbine fails to cross over the SEZ under certain wind conditions; ii) the power capture performance is unsatisfactory. Therefore, optimization techniques must be further investigated. Moreover, the performance of available control approaches is not studied in the literature, which is vital for WT designers and owners to select a control system for a WT with a SEZ. The control strategies discussed above are utilized only to maximize power production while maintaining the desired rotor speed and avoiding equipment overloads [18]. Currently, wind farms are required to play roles similar to those of conventional power plants in power systems [19]. As a result, WTs are commanded to regulate power according to the power set-points set by central control systems of wind farms. Thus, these WTs must perform three power generation tasks: power optimization, power limitation, and power regulation. These three tasks are fulfilled in a certain operation region, constrained by the rotor speed. In the case of a WT without a SEZ, it is necessary only to limit the rotor speed to the speed reference by the pitch controller under the power limitation. To date, many studies have focused on generic WTs,
295
Prated, Pm Pset b
the rated power and the measured electrical power the power set-point to the boost converter controller Pl the power set-point from the lookup torque controller Pl l , Pl h Pl in low power mode and high power mode PB, PC the power set-points at rotor speeds wB and wC PE, PF the upper and lower power limits at the speed boundaries wb and wc Pl1, Pl2, Pl3 three power limits at the speed boundary wc Ph1, Ph2, Ph3 three power limits at the speed boundary wb ttask,tcross time of control system task and set time to cross the SEZ Hs, Hm,Hl three hysteresis time Mx, My, Mz the rolling, nodding and yawing moments
especially those with doubly fed induction generators [20e24]. For a WT with a SEZ, specific control strategies must be studied, which are required to perform power generation tasks while maintaining the rotor speed outside the SEZ. However, there is no literature on such strategies. The objective of this work is to perform comparative studies of control systems for a two-bladed WT with a SEZ. Starting from available methods, this paper develops two control systems to perform power generation tasks while bypassing the SEZ. For both of them, three operation strategies are discussed, including power optimization, power limitation and power regulation. In such strategies, optimal designs are introduced to improve existing SEZ algorithms and solve their problems. Moreover, to perform power regulation outside SEZ, simple yet effective down power regulation solutions are presented. The control strategies are verified through simulations and field tests. Their performance is evaluated according to International Electro-technical Commission (IEC) standards.
2. Studied two-blade WT 2.1. Basic information The studied WT is a two-blade 3.0 MW super compact drive machine. It is manufactured by China Ming Yang Wind Power Company, and its specifications are shown in Table 1. The WT has a super compact structure, and its main body consists of two parts: the energy conversion system and its supporting tubular steel tower. The energy conversion system diagram is shown in Fig. 1, including a blade rotor, a low-ratio gearbox, a
Table 1 Specifications of the studied WT. Parameters
Value
Rotor diameter Number of rotor blades Rated electrical power Rotor speed range Nominal rotor speed Rated wind speed Rotor moment of inertia
110 m 2 3000 kW 6.0e21.0 rpm 16.2 rpm 12.2 m/s
Generator moment of inertia
2:1 103 kg$m2 23.94 3 m/s 20 m/s
Gearbox ratio Cut-in wind speed Cut-out wind speed
1:5 107 kg$m2
296
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Fig. 1. Energy conversion system diagram of the studied WT.
PMSG (permanent magnet synchronous generator) and a full-scale power converter (consisting of diode rectifiers, DC-Boost converters and grid inverters). 2.2. Characteristic curves of the studied WT By using the Bladed software application [25], the characteristic curves of the studied machine are obtained. Curves of the aerodynamic power coefficient (Cp) vs. the TSR (tip speed ratio), and thrust coefficient (Ct) vs. TSR are shown in Fig. 2. Conventionally, the pitch angle and TSR for maximum Cp acquisition are called the optimal pitch angle and optimal TSR, respectively. Fig. 2 shows that, for the studied WT, the maximum Cp is 0.454, and the corresponding optimal pitch angle and optimal TSR are 0 and 10.5, respectively. Meanwhile, the optimal pitch angle is changing in the range of 1 to 1 along with the TSR variation in the scope of 8e12. In addition, Ct increases with decreasing pitch angle when the TSR is a constant. According to [26], tower loads are proportional to the thrust coefficient. Therefore, to reduce tower loads, it is beneficial to maintain a large pitch angle and a lower TSR. Fig. 3 shows the Campbell diagram of the studied machine, in which the coupled modes are functions of rotor speeds. At a rotor speed of approximately 10 rpm, the blade passing frequency 2 P crosses the frequencies of the lowest two tower modes in the stationary frame. To avoid excessive excitation of these modes, a SEZ must be set up, which is handled by the control system studied in this work. 2.3. Control system architecture of the studied WT The control system of a modern WT is usually divided into two levels: the generator control and the WT control. These two control levels are characterized by different bandwidths [22]. For the studied turbine, a unified control architecture is adopted, running a WT and ensuring energy injection from power converters into the
electricity network at maximum efficiency [27]. Fig. 4 illustrates the architecture, in which a Siemens IPC P320 is the control unit. Based on the Profinet protocol, the power converter and other major components are controlled by one unique controller within two task periods of 250 ms and 10 ms, respectively. With this unified architecture, relations and constraints among different control levels become clear. Therefore, it turns out to be quite convenient to implement control algorithms for the WT.
3. Operation strategies of the studied WT Considering the power generation system, there are three operation tasks for modern WTs [24]: Limiting the output power to the rated power for high wind speeds (power limitation); Maximizing the power extracted from the wind for a wide range of wind speeds (power optimization); Adjusting both active and reactive powers to set-points ordered by the wind farm control system (known as power regulation operation or deloaded operation or de-rating operation). When these three tasks are executed, the rotor speed must be maintained in the predefined range. Otherwise, the machine would suffer from overload. For a generic WT with no SEZs, its rotor speed is controlled within a continuous operation zone limited by the cutin speed and the rated speed. However, for a WT with a SEZ, its rotor speed not only is constrained by the cut-in and rated speeds but also must be held away from the critical speed. Separated by the SEZ, there are two operation zones: a low speed zone and a high speed zone. Therefore, the existence of the SEZ affects such WTs’ power optimization and power regulation operations. For the sake of simplicity, SEZ-related torque control and pitch control strategies will be discussed, whereas other controls unaffected by the SEZ are
Fig. 2. The aerodynamic power coefficient and thrust coefficient curves of the studied WT.
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Fig. 3. Campbell diagram of the studied WT.
Fig. 4. Control system architecture of the studied WT.
neglected. For the studied WT, the pitch controller is used to control a hydraulic pitch system and the torque controller is used to control the DC-Boost converter. The common operation strategies employed are summarized as follows. In the power limitation condition, the operation strategy for the studied WT is mainly in charge of the pitch controller. The rotor speed is controlled to be the rated value by the pitch controller, and the generator torque is limited to the rated value by the DCBoost converter controller. As illustrated in Fig. 5, the pitch controller contains three main parts: a PD controller and two fuzzy logic units. Regarding the PD controller, its input is the error between the reference wr p and the feedback wr m , and its output is the set value of pitch speed to the hydraulic proportional valves. Two fuzzy logic units, FC1 and FC2, are designed
for the pitch bias determination and over-speed problem prevention, respectively [28]. In the power optimization condition, the torque controller is responsible for the optimized operation, and the pitch angle is maintained at its optimal value by the pitch controller. In this case, the rotor speed is controlled by the torque controllerdnot only to track the optimal TSR outside the SEZ but also to cross over the SEZ. In the power regulation condition, the operation strategy requires cooperation between the pitch controller and the torque controller. According to [24], three control strategies are available for DFIG WTs with no SEZs. Recall that down power regulation mainly involves the scheduling power and rotor speed set-points; these control strategies can also be employed by the control system of PMSG WTs. However, a special down power strategy must be developed for a WT with a SEZ.
Pset _ b Gain scheduling
FC1
wr _ p -
PD +
wr _ m
+
L1 +
dt
set
+
-
m
Com +
FC2 Fig. 5. The structure diagram of the pitch controller.
L2 +
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4. Control systems of the studied WT As mentioned previously, there are two different control approaches for a WT with a SEZ in previous works. Based on them, two control systems (denoted as Control Systems 1 and 2) are developed for the studied WT. Control System 1 is based specifically on [17], whereas Control System 2 is based on [16]. Meanwhile, optimal techniques are presented to improve conventional SEZ-crossing methods. Furthermore, power regulation strategies are proposed to fulfil power output adjustment.
4.1. Control System 1 4.1.1. Structure of Control System 1 The structure of Control System 1 is illustrated in Fig. 6, including four main parts: the pitch controller, the speed reference unit, the DC-Boost converter controller and the power set-point unit. Based on Fig. 6, the operation strategies are summarized as follows: Power limitation strategy: The reference wr p for the pitch controller is the rated value, and the power set-point Pset b for the DC-Boost converter controller is calculated based on the full powerespeed curve and wr m . Power optimization strategy: The pitch angle is maintained at its optimal value by the pitch controller, and the rotor speed is adjusted by the torque control strategy explained as follows. Power regulation strategy: The down power regulation strategy is divided into low power mode and high power mode. Both are determined by the power command Pset from the wind farm controller and the power division point PE, which corresponds to the upper power limit at the lower speed boundary of the SEZ. When Pset > PE, the WT operates in high power mode: wr p takes wr ph , which is generated from the high powerespeed curve and Pset. Meanwhile, Pset b takes Pl h , which is derived from the full powerespeed curve and wr m . When Pset PE, the WT operates in low power mode: wr p takes wr pl generated from the low powerespeed curve and Pset, whereas Pset b takes Pl l derived from the low powerespeed curve and wr m .
4.1.2. Optimized torque control scheme in Control System 1 The torque control scheme is illustrated in Fig. 7, including three parts: the power set-point unit, the bias unit, and the DC-Boost converter controller. The DC-Boost converter controller controls the generator torque. A PI controller is employed to control the Boost converter current, the set-point Iset b of which is obtained by dividing the power set-point Pset b by the rectifier's output voltage Um b . FC1, a fuzzy logic unit, is used to decouple the pitch controller and the torque controller [28]. The power set-point unit determines the powererotor speed lookup table, which includes normal points predefined according to the WT's aerodynamic data and special points related to the SEZ. In this work, eight pairs of powererotor speed points are shown in Table 2. In [17], we proved that for a twoblade WT, proper widths of the SEZ and its neighbouring zones can be ±10%. Here, the SEZ is preset to 9e11 rpm, its two neighbouring zones are defined as 8.2e9 rpm and 11e11.9 rpm, and the upper and lower power limits at two speed boundaries of the SEZ are 18% and 2%, respectively. To enhance the SEZ-bridging capability under different wind conditions, a hysteresis technique is presented to replace the predefined powererotor speed points within the SEZ. As illustrated in Fig. 8, the technique is described as follows: when the rotor speed is increased above the lower speed boundarywb(9.0 rpm), the power set-point Pset b is decreased with a certain rate to the end point PF(2.0%); when the rotor speed is decreased below the upper speed boundarywc(11.0 rpm), the power set-point is increased with a certain rate to the end point PE(18.0%). 4.2. Control System 2 4.2.1. Structure of Control System 2 Similar to Control System 1, Control System 2 also contains four main parts: the pitch controller, the speed reference unit, the DCBoost converter controller and the power set-point unit. Its structure is illustrated in Fig. 9. Based on Fig. 9, the operation strategies for the control system are as follows: Power limitation strategy: both speed references of the pitch controller and the PI torque controller are the rated value. As a
Fig. 6. Structure of Control System 1.
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Fig. 7. The torque control scheme in Control System 1.
Table 2 Powererotor speed lookup table. Measured value of rotor speed (rpm)
result, the rotor speed and generator torque are maintained at their rated value by the pitch controller and the torque controller, respectively. Power optimization strategy: the pitch angle is kept at its optimal value by the pitch controller, and the rotor speed is controlled by the PI torque control strategy. Power regulation strategy: wr p of the pitch controller and Pset b of the Boost converter controller are derived based on two power modes as mentioned earlier. When Pset > Ph3, the WT operates in high power mode: wr p takes wr ph , which is calculated from the high powerespeed curve and Pset, and wr t takes wr th , which is obtained from the full powerespeed curve and wr m . When Pset Ph3, the WT operates in low power mode: wr p takes wr pl , which is calculated from the low powerespeed curve and Pset, whereas wr t takes wr tl , which is obtained from the low powerespeed curve and wr m . Meanwhile, Pset b is derived from the output of the PI torque controller and wr m .
Power set-point (100%)
6.0 8.2 9.0 11.0 11.9 13.7 15.0 16.2
0.0 8.0 18.0 2.0 17.0 35.0 48.0
100.0
4.2.2. Optimized torque control strategy in Control System 2 The torque control strategy is illustrated in Fig. 10 and also contains three parts: the power set-point unit, the bias unit, and the boost converter controller. The Boost converter controller and the bias unit are the same as those in Control System 1. The power setpoint unit refers to a PI torque controller and a mode selection unit. The design of the PI controller is a routine with the assistance of Bladed. Here, it is worth noting that the controller gains are defined
Fig. 8. Powererotor speed curve in Control System 1.
Select mode 1: Low power mode 2: High power mode
Pset Ph3
Pset Ph3 Pset Ph3
Pset
1 wr _ p
Low powerspeed curve
Pitch controller
wr _ ph
Pset > Ph3
2 High powerspeed curve
Pset
wr _ tl Tlim it
Ph3 wr _ m
Pset Ph3
wr _ pl
Ph3
1 wr _ t Tlim it
Low powerspeed curve
wr _ th Pset > Ph3
Tlim it
2
Full power-speed curve Fig. 9. Structure of Control System 2.
PI torque controller
Pset _ b
Boost converter controller
300
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Fig. 10. The torque control scheme in Control System 2.
wA wb, wc wD and wb wc, respectively. Its value changes, when a mode transition (WT_lowhigh_transition/WT_highlow_transition) is triggered by the variation of Pm.The mode transition is determined by the time duration of the compared result between Pm and the predefined power limit. To cross the SEZ under various winds, a variable transition technique is employed. In this technique, conditions for WT_lowhigh_transition and WT_highlow_transition are summarized in Table 3. Predefined are several parametersdnamely, six power limits (Ph3,Ph2,Ph1,Pl3,Pl2 and Pl1), three hysteresis times (Hl,Hm andHs), and a crossing time tcross with two values. For the studied WT, their values are given in Table 4.
in terms of generator torque with respect to the high speed shaft. Its parameters are given as kp ¼ 8300.0[Nm/(rad/s)], k i¼ 1300.0[Nm/ (rad/s)], and the gain scheduling factor is 1.5. In addition, the optimal generator torque Topt is calculated as Topt ¼ kw2r m [29]. For the studied WT, k ¼ 14322[Nm/(rad/s)2]. Calculating the speed reference and torque limits for the PI torque controller, the mode selection unit is in charge of the SEZ algorithm. To carry out the comparison to Control System 1, the SEZ with same range of 9e11 rpm is preset. Based on the PI torque controller, the powererotor speed characteristic curve of the WT is shown in Fig. 11. In the mode selection unit, three modes are defined according to the WT's operation in different rotor speed ranges. In Fig. 11, three operation modes, named low speed mode, high speed mode and SEZ mode, correspond to rotor speed ranges of wA wb, wc wD and wb wc, respectively. The speed reference wr t and torque limit Tlimit for the PI torque controller in these modes are calculated by the algorithm described in pseudo code as follows:
As illustrated in Figs. 8 and 11, there are two powererotor speed characteristic curves for the WT with two control systems. In view of the WT performance, which is dependent on its powererotor speed characteristic curve, control systems’ impacts on the per-
In the pseudo code above, WT_speed_mode_flag is determined by measured rotor speed wr m and measured electrical power Pm. It takes one of three valuesdnamely, WT_lowspeed_mode, WT_high speed_mode and WT_TEZ_mode, based on the location of wr m in
formance in terms of power capture and tower loads can be assessed. Considering power capture, the performance of the WT with Control System 1 is inferior to that with Control System 2. On one
4.3. Assessment of two control systems
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Although a basic assessment has been obtained based on analyses of the operation principles of two control systems, it is indispensable to perform a detailed performance comparison through nonlinear simulations and field tests, which is important to give designers the confidence to choose a suitable controller for a WT with a SEZ. 5. Performance comparisons of two control systems 5.1. Comparative study based on simulation
Fig. 11. Powererotor speed curve in Control System 2.
Table 3 Transition conditions in variable transition technique. Transition type
Condition
WT_lowhigh_transition
T(Pm T(Pm T(Pm T(Pm T(Pm T(Pm
WT_highlow_transition
> > > < < <
Crossing time (tcross)
Ph3) > Hs Ph2) > Hm Ph1) > Hl Pl3) > Hs Pl2) > Hm Pl1) > Hl
ts tl tl ts tl tl
side, Control System 2 obtains better TSR-tracking than the Control System does at four speed points (wA, wb, wc and wD). On the other side, two optimal tip speed sections (wA wb andwc wD) are better handled by the PI torque control strategy in Control System 2 than by eight powererotor speed points defined in the lookup table of Control System 1. Regarding these two sides, the WT with Control System 2 would produce more power. However, the evaluation is established on the static energy balance theory, which is valid only on the premise that the WT rotor has a small inertia of moment and the winds change slowly. Regarding tower loads, the performance of the WT with Control System 1 outweighs that with Control System 2. This deduction is based on two aspects. For the first aspect, tower loads are affected by the WT's operation points outside the SEZ. According to the analyses of the resonance problem discussed in [17], tower vibration amplitude decreases with increasing difference from the critical speed. Therefore, tower loads can be determined by the degree by which the rotor speed converges to the critical speed. As illustrated in Figs. 8 and 11, Control System 2 possibly operates the WT at the speed boundary of the SEZ, whereas Control System 1works in neighbouring zones of the SEZ. In this aspect, Control System 1 produces fewer tower loads than Control System 2. For the second aspect, tower loads increase with increasing instances of SEZcrossing. The crossing instances with Control System 2 are more abundant than those with Control System 1 because the condition for SEZ-crossing is easier to satisfy in Control System 2. When the WT with Control System 1 can operate in neighbouring zones, that with Control System 2 will work at the speed boundary of the SEZ. Therefore, the WT with Control System 2 produces more tower loads than that with Control System 1.
In this section, two works are performed through detailed simulations with Bladed: control algorithm validation and performance evaluation in terms of structure loads and power production. To enhance the power capture capability, the control algorithm in Control System 2 is further improved by adjusting the optimal pitch angle. The details are as follows: the measured rotor speed and electrical power are used to examine the TSR, and pitch angles are adjusted to the optimal value based on the calculated TSR. The correlation between the optimal pitch and the TSR can be obtained by checking the Cp curves shown in Fig. 2. Therefore, three controllers are developed as the external dynamic library. Controller 1, Controller 2 and Controller 3 refer to control algorithm 1 (in Control System 1), control algorithm 2and updated control algorithm 2 (in Control System 2), respectively. In view of the fact that the simulation running time is shorter than the WT's real operation time, the hysteresis times employed by Controllers 2 and 3 are shortened to 60 s, 10 s and 1 s in simulations. 5.1.1. Validation of the proposed control algorithms Regarding SEZ-related controls, two operation scenarios, power optimization operation and power regulation operation, are considered. To validate the effectiveness of the controllers, 13 simulation tests are implemented, which are preset by the two scenarios with single point history and 3D turbulent winds. The single point history winds are set to step winds from 3 to 12 m/s, and 3D turbulent winds are defined with 6 m/s mean wind speed of three typical turbulence intensities (14%, 16%, and 18%). In this work, for the sake of simplicity, only two representative simulation results are shown; one is based on the power optimization case, and the other is the power regulation case with winds of 16% turbulence intensity. Among numerous simulation data obtained from Bladed, six signals are shown: wind speed, rotor speed, output electrical power, pitch angle, and nacelle sideeside and foreeaft accelerations. The simulation results with Controller 1, Controller 2, and Controller 3 are plotted in black, red and green, respectively. The simulation results of power optimization are illustrated in Fig. 12a. It is clear that all three controllers succeed in bridging the SEZ. However, their differences are obvious. First, the instances of SEZ-crossing are not the same. Three instances occur for Controllers 2 and 3, whereas there is only one for Controller 1. Second, before and after SEZ-crossing, Controllers 2 and 3 maintain rotor speeds nearer the speed boundaries of the SEZ than Controller 1. Third, except for several points, the WT's nacelle accelerations with Controller 1 are slightly smaller than those from other two controllers. These differences impact power capture and tower loads, which will be numerically presented in the next section.
Table 4 Parameters for the studied WT. Parameter
Hs
Hm
Hl
Ph3
Ph2
Ph1
Pl3
Pl2
Pl1
tl
ts
Value
3s
30 s
300 s
540 kW
440 kW
410 kW
200 kW
325 kW
350 kW
15 s
10 s
302
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Fig. 12. Simulation results among three controllers: (a) at power optimization case and (b) at deloaded case.
In the down power regulation case, the power regulation demand is set to 450 kW before 290 s and increased to 550 kW at 290 s with a ramping rate of 50 kW/s. The simulation results illustrated in Fig. 12b it show that all three controllers succeed in following power commands while bypassing the SEZ. Four differences are distinguishable. First, the SEZ-crossing instances are different. Three instances occur for Controllers 2 and 3, whereas
there is only one for Controller 1. Second, before and after SEZcrossing, the rotor speeds with Controllers 2 and 3 are upheld tightly to the speed boundaries of the SEZ, whereas that with Controller 1 is locate in the SEZ's neighbouring zones. Third, both the nacelle foreeaft and sideeside acceleration amplitudes with Controller 1 are obviously smaller than those with the other two controllers. Finally, pitch actions behave differently when the
J. Yang et al. / Energy 109 (2016) 294e309 Table 5 Summarized numerical results from Fig. 12a.
303
Table 7 The DELs of four components with SN4.
Controller
Mx (MNm)
My (MNm)
Mz (MNm)
Averaged power (MW)
Component
Mx (kNm)
My (kNm)
Mz (kNm)
1 2 3
3.757 7.413 7.361
7.298 8.917 8.899
1.103 1.066 1.066
0.502 0.509 0.510
Blade root (steel) Blade root (GRP) Hub (steel) Yaw bearing (steel) Tower bottom (steel)
5640.79 5591.17 393.36 452.34 5003.96
2281.65 4204.39 2491.80 2472.33 9983.03
57.40 68.64 2491.84 2482.58 2482.45
output power reaches the power demand. These differences have direct impacts on the WT's performance, which will be presented in the next section. 5.1.2. Performance comparisons with simulation results For performance comparisons, three simulation results are presented. The first two from the discussed simulation cases are used for preliminary evaluation. The third is taken for detailed comparisons, which is obtained from a complete set of simulations according to the design requirement of the IEC standard [30]. To preliminarily evaluate the WT's performance with different controllers, the averaging process function provided by Bladed is used to calculate the averaged power and averaged tower moments. The numerical results from Fig. 12a and b are summarized in Tables 5 and 6, respectively. The results of the power optimization case in Table 5 show that the averaged power production is similar between Controllers 2 and 3, as are the tower moments. Compared with Controller 2, there is a slight power output increase for Controller 3. This result proves that only the pitch angles differ in the two controllers. By comparing the results between Controller 1 and Controllers 2/3, obvious discrepancies are found. Controller 2 increases the averaged power by approximately 1.6% but doubles the tower Mx moments and increases the My moments by more than 20%. By checking the results of the deloaded case in Table 6, it is found that Controllers 2 and 3 are similar in producing power and tower moments. This fits with the fact that the trajectories of rotor speed and pitch angle in Fig. 12b are almost overlapped in the two controllers. Compared with Controller 1, Controller 2 increases the power output by more than 3.1% yet increases the tower Mx and My moments by more than 85% and 22%, respectively. Because pitch actions under the power regulation operation directly affect the aerodynamic thrust and thus the tower moments, these comparative results are different from those in Table 5. In accordance with the IEC standard [30], a complete set of simulation series is performed to calculate the design loads, which is essential to evaluate the controller impact on the loads before carrying out the field testing. In the simulation series, different winds are defined based on the analysis of wind resource measurement at the wind farm site where the studied machines are deployed: the annual average wind speed at hub height is 6.42 m/s, and the characteristic turbulence intensity at 15 m/s is 12%. Because the same pitch control algorithms and supervisory control strategies are performed in all three controllers, fatigue loads rather than extreme loads are mainly affected. Therefore, performance comparisons are conducted on fatigue loads and power production. To understand component loads, the coordinate system for load outputs should be defined. The coordinate systems of Bladed are given in the Appendix. With the coordinate systems, the damage
Table 6 Summarized numerical results from Fig. 12b. Controller
Mx (MNm)
My (MNm)
Mz (MNm)
Averaged power (MW)
1 2 3
4.723 8.640 8.592
9.115 10.880 10.812
0.805 0.973 0.972
0.381 0.393 0.393
equivalent loads (DELs) are calculated based on the assumption that the WT's lifetime is 20 years and the press cycle time is 1.0E þ 08. By using a Wohler exponent of 4 for steel and 10 for the glass reinforced plastic (GRP), the DELs of four components (steel blade root, GRP blade root, hub, yaw bearing, and tower bottom) with Controller 1 are shown in Table 7. By treating its results as the baseline, the comparative results of Controllers 2/3 are presented in Fig. 13 (the GRP blade root DELs of the three controllers are almost equal and thus are not included). The comparative results are summarized as follows: Tower bottom DELs: the Mx DEL is increased by nearly 60%, and the My DEL is increased by more than 10%. Other DELs: no significant change is found; that is, only the increments of the My DELs of the blade roots reach 5%, whereas the others are less than 3.5%. By comparing the DELs between Controllers 2 and 3, it is found that the related DELs are very similar to each other. Only an increment of 2% for the Mz DEL of the blade root is produced by Controller 3, whereas other differences are less than 1%. The optimal pitch angle adjustment applied to Controller 3 accounts for the increased Mz DEL of the blade root. To observe the contributions of different wind speeds to the tower's DELs, the tower bottom Mx and My DELs of design load case (DLC) 1.2 are shown in Fig. 14. At wind speeds of 4 m/s and 6 m/s, the tower bottom Mx and My DELs with Controllers 2 and 3 almost double those with Controller 1. The reason is that the rotor speeds with Controllers 2 and 3 at low winds are limited to the speed boundaries of the SEZ. At wind speeds of 8 m/s and 10 m/s, the tower bottom Mx DELs of the three controllers are almost equal, whereas the tower bottom My DELs of Controllers 2 and 3 are higher. This is because the rotor speeds with the other two controllers have reached the rated speed, but that with Controller 1 has not. Therefore, a larger thrust is produced by higher TSRs. Above the rated winds, slight differences among tower DELs are shown, which are affected by torque demand differences in turbulent winds. Based on the simulation results of DLC 1.2, the averaged power at different wind speeds is calculated. As shown in Fig. 15, results from Controllers 2 and 3 are compared with the baselinedthat is, the result of Controller 1. It is clear that different averaged power is produced by the three controllers. Compared with Controller 1, the other two controllers increase the power production at wind speeds of 8 m/s, 10 m/s and 12 m/s but decrease the power production at other wind speeds. The increased power production at medium wind speed is caused by the optimal TSR tracked by these two controllers. The decreased power production by less than 0.3% above the rated winds can be explained by the power loss model, which is determined by the rotor speed and generated power. The lower power production at 4 m/s and 6 m/s seems to contradict the results shown in Table 5. However, it is reasonable when considering the influence of different turbulence intensities. To assess the overall power production performance of the three controllers, the AEP (annual energy production) is calculated based on the averaged power at DLC 1.2 and the wind characteristic on the
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Fig. 13. DEL comparisons of four components among three controllers.
Fig. 14. Comparisons of tower bottom Mx and My DELs at DLC 1.2 among three controllers.
Fig. 15. Averaged electrical power comparison at DLC 1.2 among three controllers.
wind farm site. The AEP with Controller 1 is 6716.47 MWh, whereas that of Controllers 2 and 3 is slightly higher with results of 6762.78 MWh and 6764.19 MWh, respectively. Thus, there is a 0.7% difference in AEP, which must pay for a 10% increase in the tower bottom DELs. 5.2. Comparative study through field tests After validation through simulations, the control algorithms are transferred into the programmable logic controller (PLC) program and then integrated into the control systems of the studied WT. The
field testing site is located in a wind farm on the coast of southern China, in which there are ten 3 MW two-blade WTs and seven 2 MW three-blade WTs. Before the testing, the control systems of the 3 MW WTs employ the lookup table torque control algorithm. To carry out field tests, two of the ten machines, named N15 and N16, are chosen as testing objectives because their locations and their power production performance are quite similar. Control System 1 is used to update N16, and Control System 2 is tested on N15. Because Controller 3 produces more power than Controller 2 in simulations, its updated algorithm is adopted in Control System 2 for testing. The field tests were carried out in June 2015 for a duration of three weeks.
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Fig. 16. Cross over curves of SEZ on field testing for: (a) N15 with Control System 2 and (b) N16 with Control System 1.
5.2.1. Field testing results In the field testing, the control systems are tested in different wind conditions under normal grid operations. Although power regulation strategy is developed in the control system, this function is inactivated during the tests because in that wind farm, there is no such requirement to date. Because different SEZ algorithms are employed by two control systems, the results of SEZ-crossing recorded in a 10 ms period are shown in Fig. 16a and b. It can be observed that when the SEZ is crossed over, the output power varies significantly. The peaks of the output power are 750 kW and 540 kW for N15 and N16, respectively. Meanwhile, the
crossovers of the SEZ occur at different wind speeds: near 5.5 m/s for N15 and 4.5 m/s for N16. In addition, both nacelle foreeaft and sideeside accelerations increase with more transitions between two speed zones. The different acceleration amplitudes could be the results of varying winds experienced by the whole rotor. To further illustrate the different behaviour of the two control systems, another field testing result recorded for one day (24 h) is presented in Fig. 17. Because the result is with a 10 s sampling period, nacelle acceleration signals are excluded. It is very clear that the rotor speed trajectories and SEZ-crossing instances are different for N15 and N16, whereas wind conditions are surprisingly similar.
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Fig. 17. Field testing curves on one typical day (black curves for N15 and red curves for N16). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
5.2.2. Statistics analysis of field testing data Because load measurement devices are not equipped in testing WTs, only power performance evaluation is conducted by referring to the IEC standard [31]. The data collection is performed between 10 July and 10 August and recorded with 10 min averaged values. Four measurable data points (wind speed, rotor speed, output power, and pitch angle) are collected and form a valid dataset after removing corrupted data. Based on the valid dataset, 10 min averaged TSRs are computed from the instantaneous TSRs calculated at each sampling point from the measured wind and rotor speeds. Four characteristic curves of N15 and N16, including rotor speed
wind speed, pitch angleewind speed, TSRewind speed, and powerewind speed, are illustrated in Fig. 18. The former three characteristic curves are quite different, whereas the powerewind speed curves are similar. This shows that one obvious SEZ ranges from 9 to 11 rpm, and the pitch angle of N16 is maintained at 3 , whereas the pitch angle of N15 varies in the area of 2e4 . For the testing WTs, the pitch angle of 3 is the optimal pitch angle (the same as the 0 illustrated in Fig. 2). TSRs of N15 are maintained near the optimal value of 10.5 in the wind speed range of 4e5 m/s and 7e9 m/s, whereas N16's TSRs are not constant in the whole wind speed range. Meanwhile, TSRs of N15 and N16 are distributed in different
Fig. 18. Characteristic curve comparisons between N15 and N16.
J. Yang et al. / Energy 109 (2016) 294e309
Fig. 19. Averaged output power comparison between N15 and N16.
ranges. The TSRs of N15 are scattered between 9.0 and 11.5 at low winds of 4e5 m/s and between 9.8 and 11.2 at high winds of 7e9 m/s. By comparison, the TSRs of N16 are more concentrated. It means that the dynamic tracking TSR capability of N15 with Control System 2 is inferior to that of N16. To numerically compare the power capture performance of the two control systems, the averaged output power of N15 and N16 are calculated. By setting the averaged power of Control System 1 as the baseline, comparative results are shown in Fig. 19. It is obvious that N15 outputs more power below rated winds except at the wind speed of 7 m/s. This result is consistent with the TSRewind speed characteristic curve (shown in Fig. 18): at a 7 m/s wind speed, the TSRs of N15 and N16 are near the optimal value of 10.5, whereas those of N16 are much denser. Compared with the simulation results, more power is obviously produced by N15 in the low wind range (3e5 m/s), whereas the power increasing trend is similar in the high wind range (8e12 m/s). These differences can be explained by different time lengths and the influence of different turbulence, especially in low winds. Again, AEPs of N15 and N16 are calculated based on the field testing results, which are 5763.1 MWh and 5695.8 MWh, respectively. It is proved that N15 with Control System 2 produces more power than N16 with Control System 1. However, the AEP obtained from field testing results is less, approximately 15%, than that obtained by simulation, for which the possible reasons could be the wake loss and model tolerance. 6. Conclusions This paper presents a comparative study on two control systems for a two-blade WT with a SEZ, which is built to avoid tower resonance. The SEZ of the studied WT is set up and bridged by an appropriate torque control, performed through a Boost converter controller at power optimization operation in collaboration with the blade pitch control at power regulation operation. In this paper, two control systems (Control Systems 1 and 2) are developed based on existing torque control strategies, in which three operation strategies have been performed. At power optimization operation, Control System 1 employs a conventional lookup table torque control strategy, whereas Control System 2 uses a PI torque controller. To guarantee successful SEZ-crossing under different wind conditions, a hysteresis technique and a variable transition technique are performed in Control Systems 1 and 2, respectively. For power limitation operation, the two control systems use the same pitch angle controller. Regarding both
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the power regulation and the SEZ to be handled at deloaded operation, two power operation modes are divided based on the comparative result between the upper power limit of the SEZ and the power regulation command. In this way, the WT operates in the low speed range with low power command and in the full speed range with high power command. As a result, the WT can produce maximal power while maintaining its rotor speed outside the SEZ. Based on analyses of their operation principles, the impact of control systems on the WT performance is assessed: Control System 2 would produce more power at the cost of increased tower loads compared with Control System 1. The assessment is further verified through simulations and field tests. For general operation cases without down power regulation, detailed simulation tests are fulfilled according to the design requirement of IEC-64100. The simulation results illustrate the capability of developed control systems to perform the discussed tasks. Meanwhile, the simulation results show that, on the one hand, fatigue loads caused by Control System 2 are surely larger than those of Control System 1: increased DELs on other components are less than 6%, but raised tower DELs are significant, representing more than a 60% improvement and 10% increase for tower Mx DEL and My DEL, respectively; on the other hand, 0.7% greater power production is obtained by Control System 2 compared with Control System 1. The detailed numerical results have shown that the increased DELs are mainly contributed by a wind speed range corresponding to the SEZ. Following the simulation tests, field testing is implemented to validate the control systems and compare power production performance. The field testing results show that both control systems are capable of controlling the WT to build up and cross over the SEZ. Again, it has been demonstrated that energy capture performance is enhanced by Control System 2. According to a comparison of the results between simulations, an increased AEP of 1.1% is achieved by Control System 2. The simulation results also reveal that, at power regulation operation, Control System 2 produces more power than Control System 1 at the cost of increased tower loads. However, in this circumstance, there is a risk of frequent SEZ-crossings when the power regulation command is switched between high power and low power modes. Therefore, the WT would suffer from high tower loads. In this case, it is necessary to design a proper wind farm controller to send proper power commands to each WT with the SEZ. Meanwhile, deliberate evaluation strategies are necessary to carry out thorough comparisons because no applicable evaluation standard is available to follow. These aspects would be the subject of future publications. Acknowledgements This work is supported by the National Natural Science Foundation of China under Grant 61573384 and the National High Technology Research and Development Program (863 Program) of China under Grant 2015AA050604. This work is also financially supported by the Project of Innovation-driven Plan in Central South University, No. 2015CX007 and the Fundamental Research Funds for the Central Universities of Central South University under Grant 2015zzts050. Appendix The coordinate systems for load outputs in this study are defined by Bladed. They are based on the ‘GL’ convention and are shown in following figures.
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Fig. 20. Coordinate systems for load outputs.
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