APPLICATION OF STATIC VAR COMPENSATOR (SVC) WITH FUZZY CONTROLLER

FOR GRID INTEGRATION OF WIND FARM

Mehdi Narimani Rajiv K. VarmaUniversity of Western Ontario University of Western Ontario

London, Canada London, Canadamnariman@uwo.ca rkvarma@uwo.ca

ABSTRACT

Large-scale integration of wind turbine generators (WTGs) may have significant impacts on power system operation with respect to bus voltages, system frequency, etc. Voltage control and reactive power compensation in a weak distribution network for integration of wind power represents the main concern of this paper. Without reactive power compensation, the integration of wind power in a network may potentially cause voltage collapse in the system and under-voltage tripping of wind power generators. This paper shows that while static compensation (Fixed Capacitor Bank) is unable to prevent voltage collapse, dynamic reactive power compensation using Static Var Compensator (SVC) at the a point of interconnection of wind farm is successful in maintaining acceptable voltage level. Moreover, this paper shows that by using a fuzzy controller instead of a PI controller, the performance of SVC is improved.

MATLAB/Simulink based simulation is utilized to demonstrate the application of SVC in wind farm integration and the enhancement in performance achieved with a fuzzy controller as compared to a Proportional Integral controller for voltage regulation during fault scenarios.

Index Terms— Induction generators, Reactive power compensation, SVC, Voltage regulation, fuzzy controller, Wind power

1. INTRODUCTION

A pressing demand for more electric power coupled with depleting natural resources has led to an increased need for energy production from renewable energy sources such as wind and solar [1]. Wind turbines with 2-3 MW capability are already commercially available and a 5-MW wind turbine also will be available in a few years. Moreover, the cost of wind energy has been reduced to 4.5 cents/kWh which is very competitive as compared to conventional

fuels. This is expected to be further reduced to 3 cents/kWh for utility-scale wind energy onshore and 5 cents/kWh offshore by 2012 [2]. However, with an increasing share derived from wind power sources, large scale connection of wind farms to the system has brought in new challenges [1-3]. Voltage stability and efficient fault ride through capability are the basic requirements for higher penetration. Wind turbines are expected to provide uninterrupted operation under transient voltage conditions in accordance with the grid codes.

Low-cost mechanical switched capacitor (MSC) banks and transformer tap changers (TCs) are conventionally used to address issues related to voltage control. Although these devices help improve the power factor of wind turbine and provide steady-state voltage regulation, the power quality issues, such as power fluctuations, voltage fluctuations, and harmonics, cannot be solved satisfactorily by them because these devices are not fast enough [2]. Moreover, frequent switching of MSC and TC to deal with power quality issues may even cause resonance and temporary overvoltages, additional stresses on wind turbine gearbox and shaft making turbines wear out quickly and, hence, increasing the maintenance and replacement cost [4]. Therefore, a dynamic shunt reactive power compensator is needed to address these issues more effectively. Flexible AC Transmission System (FACTS) devices such as the Static Var Compensator (SVC) are being used extensively in power systems as they significantly improve system stability, especially during weak network conditions [5]. SVCs have the ability to provide voltage support either by supplying and/or absorbing reactive power into the network at the point of common coupling (PCC).

In this paper the reactive power compensation capability of SVC for wind power integration into a weak distribution network is evaluated. SVC performance is evaluated with both a conventional Proportional –Integral (PI) controller and a Fuzzy. The study is based on the three phase non-linear dynamic simulation, utilizing the SIMPOWERSYSTEM and FUZZY blocksets with MATLAB/Simulink.

2. EFFECT OF SVC ON WIND FARM INTEGRATION

2.1. System Specifications

Wind turbines using fixed speed induction generator (FSIG) provide a simple and cost effective solution for wind farm integration and are therefore considered in this paper [6]. An important operating characteristic of the squirrel cage induction generator is that this type of generator always consumes reactive power, which is undesirable for the transmission system, particularly in the case of large turbines and weak distribution systems. Another characteristic of the squirrel cage induction generators is that, in general, this type of generator tends to slow down voltage restoration after a disturbance, which can lead to voltage and rotor speed instability. When the voltage restores, the generator consumes reactive power, impeding the voltage restoration. When the voltage does not return quickly enough, the generator continues to accelerate and consumes even larger amount of reactive power [3]. This process eventually leads to voltage and rotor speed instability if the wind turbine is connected to a weak system. For solving this problem, an SVC may be utilized in power systems. The single line diagram of the study system is shown in Fig.1. A 25 kV distribution system is fed by a 150 kV grid bus through a 150/25 kV, 100 MVA step down transformer. Three loads; one load of 50 MW at the transformer, 0.93 pf (lag), one load 12 MW 0.93 pf (lag) at 30 km and another load is 6 MW with 0.98 pf (lag) at 50 km from the transformer. Two 25 kV lines are considered, one line is 30 km and the other one is 50 km long. A 9 MW wind farm consisting of six 1.5 MW wind turbines is connected to the 25 kV distribution network at 6 MW load bus. The 9 MW wind farm has conventional wind turbine systems consisting of squirrel-cage induction generators and variable pitch wind turbines. In order to limit the generator output power to its nominal value, the pitch angle is controlled for winds exceeding the nominal speed of 9 m/s. Each wind turbine generator set has a capacitor bank for power factor correction.

Grid Supply150 KV

BUS 1

BUS 2

BUS 3

Load 1

Load 2

Load 3

SVC

Wind Farm9 MW

30 km Line

50 km Line

Transformer150 / 25 KV

Fig. 1. Single line diagram of the study system

2.2. SVC Model

Fig. 2 shows the configuration of the SVC used in this study. It consists of 3 Thyristor Switched Capacitors (TSC) in shunt with a Thyristor Controlled Reactor (TCR). The SVC can be operated to provide reactive power control or closed-loop AC voltage control. For closed-loop AC voltage control, the line voltage, as measured at the point of connection, is compared to a reference value and an error signal is produced. This is passed to a PI controller to generate the required susceptance value. It is then transmitted to the non-linear admittance characteristic to generate the firing angle for the TCR and to determine the number of TSC stages need to be switched on. The firing angle is passed to the gate pulse generator, which then generates the firing pulse for the TCR.

TSC1

2 Mvar

TCR

2.5 Mvar

TSC2

2 Mvar

TSC3

2 MVar

Power System

A B C

P A B CP A B CP A B CP A B C

Secondary(16 KV)

A

B

C

a

b

c

SVC Controller

Vabc_prim

Vabc_sec

TCR

TSC1

TSC2

TSC3

Primary(33 kV)

A

B

C

a

b

c

Vabc_Prim

Vabc_Sec

33 KV/ 16KV6 MVA

A

B

C

a

b

c

Fig. 2. Schematic diagram of SVC with 3 TSC and 1 TCR

The PI Controller used in this study is tuned with a systematic hit and trial method to provide the fastest response with a settling time of less than 4 cycles and an overshoot less than 10%.

2.2.1. Fuzzy Controller

One problem in design of a SVC for good performance is the tuning of PI controller which may not be achieved in a simplistic manner. In this paper, a fuzzy controller design for SVC is presented. Fuzzy controller is one of the nonlinear and robust control methods which is based on expert knowledge and there is no need to have the accurate model of the system. There are two main types of Fuzzy Logic Controllers (FLCs): Mamdani 's type and Takagi-Sugeno (T-S) [7]. Due to the simplicity of Mamdani's model and ease of implementation in hardware, Mamdani’s type is considered in this paper. The ith rule of a Mamdani type FLC is given in [7]:

i iif e is A then u is C

,e u Are linguistic variables with the sets that are written as the following:

� �

SmallPositivePSMediumPositivePM

BigPositivePBSmallNegativeNSMediumNegativeNM

BigNegativeNBZeroZE

PBPMPSZENSNMNBS

�

�

�

�

�

��

� ,,,,,,

The membership functions of these variables are shown in figure 3 and 4.

-10 -8 -6 -4 -2 0 2 4 6 8 10

0

0.2

0.4

0.6

0.8

1

1.2

Input

Deg

ree o

f m

em

bers

hip

NB NM NS ZE PE PM PB

Fig. 3. Fuzzy sets for error ( )(eAi )

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

0

0.2

0.4

0.6

0.8

1

1.2

Output

Deg

ree o

f m

em

bers

hip

NB NM NS ZE PS PM PB

Fig. 4. Fuzzy sets for control signal ( )(yCi )The rules that are used for control of SVC are shown in Table1.

TABLE I. Base Rules for Fuzzy Controller

ueRules PBPBR1 PMPMR2 PSPSR3 NBNBR4 NM NM R5 NSNSR6 ZEZER7

For example R2 is:

PMisuthenPMiseifR :2

and control signal is:

�

�

�

�� r

ii

r

iii

W

FWu

1

1

Where:

���

dyyC

dyyyCF

i

ii )(

)(

( ) 1,2,...,7i iW A e i� �

( )iA e is degree of membership of e . And iF is the defuzzification of output.

3. SYSTEM STUDIES

The following four studies are conducted :

1- Steady state system studies without wind farm 2- Steady state studies with wind farm but without

SVC3- Steady state studies with both wind farm and

SVC4- Fault studies

3.1. Steady State System Performance without Wind Farm

The purpose of this study is to demonstrate the performance of the weak study system. In this mode, the voltages at 25 kV Bus1 (Load1), Bus2 (Load2) and Bus3 (Load3) are measured. The voltage at 25 kV Bus1 is about 0.95 pu whereas the voltage at Bus 2 and 3 are 0.82 pu and 0.86, respectively. As these voltages are below the acceptable value of 0.95 pu the distribution network taken for this study is indeed shown to be weak.

3.2. Steady state system performance with wind farm and without SVC

The purpose of this study is to study the integration of 9 MW wind power in the weak distribution network, without dynamic compensation of reactive power. Figure 5 shows that by connecting the wind farm, the voltage at 25 kV Bus 2 is low, thereby causing an under voltage tripping of wind turbine generators. Fig. 6 shows that the wind turbine generators are tripped one by one due to the lack of sufficient reactive power which can not be provided by the network capacitors. This figure shows that the wind turbine generators power cannot be integrated in a weak distribution network. The reactive power drawn by different wind turbine generators (WTGs) is shown in Fig. 7. The active and reactive power at various buses in the network are shown in Figs. 8 and 9, respectively.

0 2 4 6 8 10 12 14 16 18 200.5

0.6

0.7

0.8

0.9

1

Time (Sec)

Vo

ltag

e (

pu

)

Bus 1

Bus 3

Bus 2

Fig.5. Voltages at 33 kV Bus1-3

0 2 4 6 8 10 12 14 16 18 20-1

0

1

2

3

4

Time (Sec)

P 1

-3 (

MW

)

P1P3P2

Fig. 6. Active power supplied by WTGs

0 2 4 6 8 10 12 14 16 18 20-1

0

1

2

3

4

Time (Sec)

Q 1

-3 (

MW

)

Q1

Q2

Q3

Fig. 7. Reactive power drawn by WTGs

3.3. Steady state system performance with wind farm and SVC

The purpose of this study is to integrate 9 MW wind power in the weak distribution network utilizing a SVC. The SVC rating is determined from load flow studies to provide 1 p.u voltage at the point of connection. It is obtained to be +6 MVAr capacitive and -2.5 MVAr inductive.

Figure 10 shows that due to reactive power injection, the SVC facilitates wind farm integration and provides voltage regulation. Active power supplied to the network is shown in Fig. 11 and the reactive power which is absorbed from the network is shown in Fig. 12. These figures show that the wind turbine generators are supplying 9 MW power to the network.

0 2 4 6 8 10 12 14 16 18 20

-60

-40

-20

0

20

40

Time (Sec)

P (

MW

)

Load 1Load 2

Load 3

Bus 2

Bus 1

Fig. 8. Active power at various buses in the network

0 2 4 6 8 10 12 14 16 18 20

-30

-20

-10

0

10

20

Time (Sec)

Q (

MW

)

Load 1Load 2

Load 3

Bus 1

Bus 2

Fig.9. Reactive power at various buses in the network

0 2 4 6 8 10 12 14 16 18 200.5

0.6

0.7

0.8

0.9

1

Time (Sec)

Vo

ltag

e (

pu

)

Bus 3

Bus 1

Bus 2

Fig. 10. Voltages at 33 kV Bus1-3

0 2 4 6 8 10 12 14 16 18 20-1

0

1

2

3

4

Time (Sec)

P 1

-3 (

MW

)

P1 P2 P3

Fig. 11 Active power supplied by WTGs

0 2 4 6 8 10 12 14 16 18 200

0.5

1

1.5

2

2.5

Time (Sec)

Q 1

-3 (

MW

)

Q1

Q2 Q3

Fig. 12. Reactive power drawn by WTGs

0 2 4 6 8 10 12 14 16 18 20

-60

-40

-20

0

20

40

Time (Sec)

P (

MW

)

Load 1

Bus 2

Bus 1

Load 3

Load 2

Fig. 13. Active power at various buses in the network

0 2 4 6 8 10 12 14 16 18 20

-20

-10

0

10

20

Time (Sec)

Q (

MW

)

SVC

Load 1

Load 2

Load 3

Bus 2

Bus 1

Fig.14. Reactive power at various buses in the network

The active and reactive power at various buses in the network are shown in Fig.13 and 14 respectively. SVC is supplying about 4.5 MVAr reactive power to the network to support grid integration by wind farm. The SVC not only can supply reactive power to the network but also can regulate the bus voltage depending on its rating.

3.4. Fault studies

A solid three-phase-to-ground fault is initiated at Bus 3 connected to the bus wind farm and cleared by opening the breaker. The three-phase to ground fault was applied at 1.5s and cleared after 140ms. System performance with fixed network capacitor alone, are depicted in Fig. 15. As can be seen from Fig. 16, during the fault, due to the reduction of the AC voltage, the generated active power and the electric torque are significantly reduced. The system becomes unstable and the wind farm has to be disconnected from the network.

3.4.1 SVC with PI Controller

With the same network configuration and with SVC connected, the results are shown in Fig. 16. As can be seen, the AC voltage recovers after the clearance of the fault due to the dynamic reactive power support from the SVC. The amount of reactive power provided by the SVC is about 5.5 MVAr. The system is now stable and satisfactory integration of the wind farm is achieved.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.2

0.4

0.6

0.8

1

Vo

ltag

e (

pu

)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.9

1

1.1

1.2

1.3

Time (Sec)

Sp

eed

(p

u)

Fig. 15. Simulated results with PFC only

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.5

1

1.5

Vo

ltag

e (

pu

)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.98

1

1.02

1.04

1.06

Time (Sec)

Sp

eed

(p

u)

Fig.16. System behaviour with SVC using PI controller

3.4.2 SVC with Fuzzy Controller

With the same network configuration and using SVC with fuzzy controller, the results are shown in Fig. 17.

3.4.3 Comparison of SVC based on PI controller and Fuzzy Controller

The bus voltage, TCR firing angle and number of TSCs in service for SVC with PI controller are depicted in Figure 18, and the same variables for SVC with Fuzzy controller are illustrated in Figure 19. It is seen that the fuzzy controller results in a better performance in terms of overshoot and settling time rather than a PI controller which is tuned with the best coefficients. It is also seen that the control signals applied to TCR and TSCs by fuzzy controller are smoother than they are with PI Controller.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

0.2

0.4

0.6

0.8

1

Vo

ltag

e (

pu

)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50.98

1

1.02

1.04

1.06

1.08

Time (Sec)

Sp

eed

(p

u)

Fig.17. System behaviour with SVC using Fuzzy controller

0 0.5 1 1.5 2 2.5 30

0.5

1

Vo

ltag

e (

pu

)

SVC with PI Controller

0 0.5 1 1.5 2 2.5 3120

140

160

180

TC

R f

ire A

ng

le (

deg

)

0 0.5 1 1.5 2 2.5 30

1

2

3

Time (Sec)

Nu

m.

of

TS

Cs

Fig.18. System behaviour with SVC using PI Controller

0 0.5 1 1.5 2 2.5 30

0.5

1

Vo

ltag

e (

pu

)

SVC with Fuzzy Controller

0 0.5 1 1.5 2 2.5 3120

140

160

180

TC

R f

ire a

ng

le (

deg

)

0 0.5 1 1.5 2 2.5 30

1

2

3

Time (Sec)

Nu

m.

of

TS

Cs

Fig. 19. System behaviour with SVC using Fuzzy Controller

4. CONCLUSION

This paper presents an evaluation study of the dynamic power compensation capability of SVC for successful integration of wind power in a weak distribution network. The performance of SVC is examined during an external three phase fault using MATLAB/Simulink. The SVC provides regulation of bus voltage and also a rapid recovery of voltage after fault is cleared. A Fuzzy controller is designed for the SVC which is shown to provide a much improved SVC response as compared to a conventional PI controller.

5. REFERENCES

[1] A.P. Jayam, B.H. Chowdhury, “Improving the dynamic performance of wind farms with STATCOM”, Power Systems Conference and Exposition, PES '09. IEEE/PES, pp. 1 – 8, 15-18 March 2009.

[2] H. Chong , A.Q. Huang, M.E. Baran, S. Bhattacharya, W. Litzenberger, L. Anderson, A.L. Johnson, A. Edris, “STATCOM Impact Study on the Integration of a Large Wind Farm into a Weak Loop Power System”, IEEE Trans. on Energy Conversion, Volume 23, Issue 1, pp. 226 – 233, March 2008

[3] S.M. Shinde, K.D. Patil, W.Z. Gandhare, “Dynamic compensation of reactive power for integration of wind power in a weak distribution network” Proc. 2009 International Conference on Control, Automation, Communication and Energy Conservation, INCACEC 2009, pp. 1 – 6, 4-6 June 2009.

[4] A. Kehrli, M. Ross, “Understanding grid integration issues at wind farms and solutions using voltage source converter FACTS technology”, Power Engineering Society General Meeting, 2003, IEEE Volume 3, 13-17 July 2003.

[5] Mohan Mathur, R., Varma, R.K., “Thyristor-based FACTS controllers for electrical transmission systems”, IEEE Press and Wiley , New York, 2002.

[6] L. Xu, L. Yao, C. Sasse, “Comparison of Using SVC and STATCOM for Wind Farm Integration” Power System Technology, International Conference on Power Electronics, pp. 1 – 7, 2006.

[7] C. Lee, “Fuzzy Logic in Control Systems: Fuzzy Logic Controllers - Part I”, IEEE Trans on Systems, Manand Cybernetics, Vol. 20, No.2, 1990.