Dynamic Behavior of DFIG Wind Turbine Under Grid Fault Conditions

 

ABSTRACT:

The use of doubly fed induction generators (DFIGs) in wind turbines has become quite common over the last few years. These machines provide variable speed and are driven with a power converter which is sized for a small percentage of the turbine-rated power. This paper presents a detailed model of induction generator coupled to wind turbine system. Modeling and simulation of induction machine using vector control computing technique is done. DFIG wind turbine is an integrated part of distributed generation system. Therefore, any abnormalities associates with grid are going to affect the system performance considerably. Taking this into account, the performance of DFIG variable speed wind turbine under network fault is studied using simulation developed in MATLAB/SIMULINK.

KEYWORDS

  1. DFIG
  2. DQ Model
  3. Vector Control

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig. 1 Simulink model of DFIG system

EXPECTED SIMULATION RESULTS:

 Time (sec)

 Fig. 2 Stator currents during balance condition

Time (sec)

Fig. 3 Rotor currents during balance condition

   Time (sec)

Fig. 4 Speed and torque during balance condition.

Time (sec)

Fig. 5 Acive and reactive power during balance condition

CONCLUSION:

This paper presents a study of the dynamic performance of variable speed DFIG coupled with wind turbine. The dynamic behavior of DFIG under power system disturbance was simulated using MATLAB/SIMULINK.Accurate transient simulations are required to investigate the influence of the wind power on the power system stability. The DFIG considered in this analysis is a wound rotor induction generator with slip rings. The stator is directly connected to the grid and the rotor is interface via a back to back power converter. Power converter are usually controlled utilizing vector control techniques which allow the decoupled control of both active and reactive power flow to the grid. In the present investigation, the dynamic DFIG performance is presented for both normal and abnormal grid conditions. The control performance of DFIG is satisfactory in normal grid conditions and it is found that, both active and reactive power maintains a study pattern in spite of fluctuating wind speed and net electrical power supplied to grid is maintained constant.

REFERENCES:

[1] T. Brekken, and N. Mohan, “A novel doubly-fed induction wind generator control scheme for reactive power control and torque pulsation compensation under unbalanced grid voltage conditions”, IEEE PESC Conf Proc., Vol 2, pp. 760-764, 2003.

[2] L. Xu and Y. Wang, “Dynamic modeling and control of DFIG-based wind turbines under unbalanced network conditions”, IEEE Trans. On Power System, Vol 22, Issues 1, pp. 314-323, 2007.

[3] F.M. Hughes, O. Anaya-Lara, N. Jenkins, and G. Strbac, “Control of DFIG based wind generation for power network support”, IEEE Trans. On Power Systems, Vol 20, pp. 1958-1966, 2005.

[4] S. Seman, J. Niiranen, S. Kanerva, A. Arkkio, and J. Saitz, “Performance study of a doubly fed wind-power induction generator Under Network Disturbances”, IEEE Trans. on Energy Conversion, Vol 21, pp. 883-890, 2006.

[5] T. Thiringer, A. Petersson, and T. Petru, “Grid disturbance response of wind turbines equipped with induction generator and doubly-fed induction generator”, in Proc. IEEE Power Engineering Society General Meeting, Vol 3, pp. 13-17, 2003.

 

Fuzzy Controller for Three Phases Induction Motor Drives

ABSTRACT:

Because of the low maintenance and robustness induction motors have many applications in the industries. Most of these applications need fast and smart speed control system. This paper introduces a smart speed control system for induction motor using fuzzy logic controller. Induction motor is modeled in synchronous reference frame in terms of dq form. The speed control of induction motor is the main issue achieves maximum torque and efficiency. Two speed control techniques, Scalar Control and Indirect Field Oriented Control are used to compare the performance of the control system with fuzzy logic controller. Indirect field oriented control technique with fuzzy logic controller provides better speed control of induction motor especially with high dynamic disturbances. The model is carried out using Matlab/Simulink computer package. The simulation results show the superiority of the fuzzy logic controller in controlling three-phase induction motor with indirect field oriented control technique.

 KEYWORDS:

  1. Vector control
  2. Fuzzy logic
  3. Induction motor drive

 SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

image001

Fig. 1. Block diagram of scalar controller for IM.

image002

Fig. 2. Indirect Field Oriented Control of IM.

 EXPECTED SIMULATION RESULTS:

 image003

Fig. 3. Speed response of scalar and vector control

image004

Fig 4. Torque response of scalar and vector control.

image005

Fig. 5. Flux response of scalar control.

image006

Fig. 6. Flux response of vector control.

CONCLUSION:

Fuzzy logic controller shows fast control response with three-phase induction motor. Two different control techniques are used with Fuzzy logic controllers which are scalar and field oriented control techniques. Fuzzy logic controller system shows better response with these two techniques. Meanwhile, the scalar controller has a sluggish response than FOC because of the inherent coupling effect in field and torque components. However, the developed fuzzy logic control with FOC shows fast response, smooth performance, and high dynamic response with speed changing and transient conditions.

 REFERENCES:

 [1] A. Mechernene, M. Zerikat and M. Hachblef, “Fuzzy speed regulation for induction motor associated with field-oriented control”, IJ-STA, volume 2, pp. 804-817, 2008.

[2] Leonhard, W.,” Controlled AC drives, a successful transfer from ideas to industrial practice”, CETTI, pp: 1-12, 1995.

[3] M. Tacao, “Commandes numérique de machines asynchrones par lagique floue”, thése de PHD, Université de Lava- faculté des science et de génie Québec, 1997.

[4] Fitzgerald, A.E. et al., Electric Machinery, 5th Edn, McGraw-Hill, 1990.

[5] Marino, R., S. Peresada and P. Valigi, “Adaptive input-output linearizing control of induction motors”, IEEE Trans. Autom. Cont., 1993.