FACTS Devices Using Neuro Fuzzy Controller in Stabilization of Grid Connected Wind Generator.

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FACTS Devices Using Neuro Fuzzy

Controller in Stabilization of Grid

Connected Wind Generator.

ROHI KACHROO*

IV sem M Tech, GHRCE, Nagpur University Nagpur 16, India†

kachroo.rohi@rediffmail.com

H.S. DALVI

Astt prof. GHRCE, Nagpur University Nagpur 16, India

hsdalvi123@yahoo.com Abstract:

Wind power is one of the renewable energy sources. It has various advantages like, cost competitiveness, environmentally clean and safeness. Large wind farms have stability problems when they are integrated to the power system. A thorough analysis is required to identify the stability problems and to develop measures to improve it. Mostly used wind generator is a fixed speed induction generator, which requires reactive power to maintain air gap flux. Reactive power equipments are used to enable recovery of large wind farms from severe system disturbances. In this paper shunt and series FACTS devices, Static Synchronous Compensator (STATCOM) and Static Synchronous Series Compensator are used for the purpose of stabilizing grid connected wind generator against the grid-side disturbances. The essential feature of the FACTS devices is their ability to absorb or inject the reactive power. Since stability is a non linear process so system performance can be improved by using nonlinear controllers. Neurofuzzy controller (NFC) is a non linear controller. NFC has faster response than conventional PI controllers.

Keywords: Renewable Wind Energy (WE), wind farm, system, Induction generator (IG), FACTS devices,

reactive power compensation, Neuro Fuzzy controller (NFC)

1. Introduction

A continuous effort to increase the generation of electrical power from renewable sources is increasing because the existing conventional power generation sources have considerable limitations like continually diminishing reserves and severe impact on the environment.

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2. Wind Turbine Model

Wind turbines use squirrel cage induction generators are shown in Fig. 1. The stator winding is connected directly to the grid and the rotor is driven by the wind turbine. The electrical power is generated by the induction generator with the help of wind turbine and is transmitted to the grid by the stator winding. To limit the generator output power to its nominal value for high wind speeds, pitch angle is controller is used.

Fig 1 Wind Turbine and induction Generator

The mechanical power extracted from the wind can be expressed as follows

Where, Pm is the extracted power from the wind, is the air density, R is the blade radius (m), is the wind speed (m/s) and is the power coefficient which is a function of tip speed ratio λ , and blade pitch angle

β(deg).

3. STATCOM & SSSC Model

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Fig.2 Schematic Diagram of control system of a SSSC

Fig.3 Schematic Diagram of control system of a STATCOM

4. Neuro Fuzzy Controller Modelling

NFC is a hybrid of neural network (NN) and fuzzy system (FS). Neuro fuzzy systems are known for their capabilities of combining the advantages of FS and NN. In this paper 80% of NN and 20 % of FS output is combined to modify the overall output.

Fig 4(a) STATCOM Fuzzy Logic controller

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Fig 5 Membership Function

The fuzzy IF-THEN rules are as follows

IF ( ev = N ) and ( cev = N )THEN (output = P) IF ( ev = N ) and ( cev = Z ) THEN (output = P) IF ( ev = N ) and ( cev = P ) THEN (output = Z) IF ( ev = Z ) and ( cev = N ) THEN (output = P) IF ( ev = Z ) and ( cev = Z ) THEN (output = Z) IF ( ev = Z ) and ( cev = P ) THEN (output = N) IF ( ev = P ) and ( cev = N ) THEN (output = Z) IF ( ev = P ) and ( cev = Z ) THEN (output = N)

IF ( ev = P ) and ( cev = P ) THEN (output = N)

Incase of STATCOM output is Iqref & SSSC output is vq_conv. Neural network designed is a feed forward two layer network with 20 neurons. The network is trained by Leaven berg Marquardt method. Neurofuzzy controller designed is shown in Fig 6(a) & Fig 6(b).The NFC is connected in the AC voltage regulator of STATCOM block and injected voltage regulator of SSSC block available in mat lab library shown in Fig 7(a) Fig 7(b)

Fig 6 (a) STATCOM Neuro fuzzy controller (NFC)

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Fig 7(a) NFC connected in AC voltage regulator of STATCOM

Fig 7(b) NFC connected in injected voltage regulator of SSSC

5. System Model

Studied wind farm in this paper has 6, 1.5 MW turbines. Thus the wind farm is having capacity of 9 MW. Mentioned units are connected to grid by a 400/25 kV transformer and a 25 kV, 2 lined distribution line with 25 km length and 132/25 kV transformer. Used generators in this model are squirrel cage induction generators and stator windings are directly connected to the grid and at the junction point in order to compensate part of required reactive power, capacitor bank is used. Simulated system model is shown in Fig 8 (a) for STATCOM & Fig 8 (b) for SSSC

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This grid is used to study and analyse machine and wind farm stability. A Short circuit effect close to the CB occurs on transmission line occurs at 15s and is cleared after 200ms

6. Simulation Analysis

It is clear that reactive power is supplied by the grid to the stator winding of the induction generator for development of the rotating magnetic field at the stator. The reactive power demand compensation at steady state is provided by the capacitor bank which is inserted across the terminals of wind turbines. 3LG fault occurs at 25KV network in the transmission line at 15 sec & is cleared after 200 ms i.e. fault is cleared at 15.2 sec. It is seen that during the fault, whole of WPP units trip and exit from the circuit as shown in Fig 9(a) & Fig 9(b). Tripping is because of grid unable to fulfill reactive power of WPP as shown in Fig 10 (a) & Fig 10 (b) .The voltage at bus B2 drops from unity as shown in Fig11 (a) & Fig 11 (b) and rotor of the wind turbines accelerates out of control as shown in Fig 12 (a) & Fig 12 (b) .This leads to instability conditions of the grid .When STATCOM or SSSC is connected reactive power is generated thereby fulfilling the reactive power requirement of WPP shown by Fig 10(a) & Fig 10 (b) , preventing their tripping as shown by Fig 9(a) & Fig 9 (b).Voltage at bus B2 is improved as shown in Fig 11 (a) & Fig 11 (b). Turbine rotor accelerates but is under control as shown in Fig 12 (a) & Fig 12 (b). PI controller of STATCOM and SSSC is replaced by NFC and system performance is improved as shown in Fig 9(a) & 9(b), Fig 10(a) & 10(b), Fig 11(a) & 11(b) and Fig 12 (a) & 12 (b). The settling time of the system has reduced making system relatively stable.

7. Conclusion

It is shown that FACTS devices like STATCOM and SSSC can enhance the stability of WPP as well as of entire power system when severe network disturbances occur in power system. It is clearly presented that STATCOM & SSSC equipped with the neuro fuzzy control gives better performance faster response than conventional PI controller. In the system under consideration response of neuro fuzzy STATCOM is better than neuro fuzzy SSSC as it is clear from Fig 13 , Fig 14 & Fig 15.System is relatively stable with neuro fuzzy STATCOM than neuro fuzzy SSSC. So it is better to connect STATCOM with NFC as it can enhance stability and improve performance of grid connected wind generator better than SSSC.

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Fig 9(b) Active power (Mw) at bus B2 generated by 9Mw wind farm in SSSC

Fig 10(a) Reactive power (Mvar) requirement at bus B2 in STATCOM

Fig 10(b) Reactive power (Mvar) requirement at bus B2 in SSSC

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Fig 11(b) Voltage ( pu ) at bus B2 in SSSC

Fig 12(a) Turbine Rotor (pu) in STATCOM

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Fig. 13 Voltage at bus at B2

Fig. 14 Active Power at bus at B2

Fig.15 Rotor Speed of Wind Turbines

References

[1] HAN Pingping , DING Ming and LI Binbin, “Study on transient stability of grid connected Large scale wind power system” 2010 2nd

IEEE international symposium on power electronics for distributed generation systems pp 622-625

[2] Hideyuki Takagi Kyushu institute of design, Fukuoka, Japan “Fusion Technology of Neural Networks and Fuzzy Systems: A

Chronicled Progression from the Laboratory to our Daily Lives” International Journal of Applied Mathematics and Computer Science pp 1-10

[3] Jaun M Ramirez, ruben Tapia O., Julio C. Rosas, Jose A. Vega ,“ Voltage Regulation by Neuro controller” IEEE 2007 pp 1-7

[4] JianWu,Dian-guo Xu,and Na He,Harbin institute of technology,china,”Self tunning fuzzy control for shunt active power filter

[5] M. Jazayeri (1), M. Fendereski , Semnan University, Semnan, Iran Islamic Azad University Science & Research Branch, Tehran, Iran

“Stablization of Grid Connected Wind Generator During Power Network Disturbances By STATCOM” UPEC pp 1181-1186 Issue 2007

[6] M. Tarafdar Hagh, A. Roshan Milani, A. Lafzi Center of Excellence for Mechatronics, University of Tabriz, Tabriz, Iran 2 Faculty of

Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran “Dynamic Stability Improvement of a Wind Farm Connected to Grid Using STATCOM” IEEE Proceedings of ECTI, pp 1057-1060 Issue 2008

[7] N.G Hingorani,LGyugy, “Understanding FACTS; Concepts and Technology of Flexible AC Transmission Systems” IEEE Press ,New

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Figure

Fig 1 Wind Turbine and induction Generator

Fig 1

Wind Turbine and induction Generator p.2
Fig. 14 Active Power at bus at B2
Fig. 14 Active Power at bus at B2 p.9
Fig. 13 Voltage at bus at B2
Fig. 13 Voltage at bus at B2 p.9

References