Design and Implementation of Neuro-Fuzzy Vector Control for Wind-Driven Doubly-Fed Induction Generator

Wound-rotor induction generators have numerous advantages in wind power generation over other types of generators. One scheme is realized when a converter cascade is used between the slip-ring terminals and the utility grid to control the rotor power. This configuration is called the doubly-fed induction generator (DFIG). In this paper, a vector control scheme is developed to control the rotor side voltage source converter that allows independent control of the generated active and reactive power as well as the rotor speed to track the maximum wind power point. A neuro-fuzzy gain tuner is proposed to control the DFIG. The input for each neuro-fuzzy system is the error value of generator speed, active or reactive power. The choice of only one input to the system simplifies the design. Experimental investigations have also been conducted on a laboratory DFIG to verify the calculated results.

[1]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[2]  Narayan C. Kar,et al.  Fuzzy Gain Tuner for Vector Control of Doubly-Fed Wind Driven Induction Generator , 2006, 2006 Canadian Conference on Electrical and Computer Engineering.

[3]  Gautam Poddar,et al.  Sensorless variable-speed controller for existing fixed-speed wind power generator with unity-power-factor operation , 2003, IEEE Trans. Ind. Electron..

[4]  R. Cardenas,et al.  Sensorless vector control of induction machines for variable-speed wind energy applications , 2004, IEEE Transactions on Energy Conversion.

[5]  N.C. Kar,et al.  Adaptive vector control for slip energy recovery in doubly-fed wind driven induction generator , 2005, Canadian Conference on Electrical and Computer Engineering, 2005..

[6]  Longya Xu,et al.  A flexible active and reactive power control strategy for a variable speed constant frequency generating system , 1993, Proceedings of IEEE Power Electronics Specialist Conference - PESC '93.

[7]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[8]  Narayan C. Kar,et al.  Leakage flux saturation effects on the transient performance of wound-rotor induction motors , 2008 .

[9]  Eel-Hwan Kim,et al.  Power factor control of a doubly fed induction machine using fuzzy logic , 2001, ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501).

[10]  P. Vas.  Vector control of AC machines , 1990 .

[11]  G. Tapia,et al.  Modeling and control of a wind turbine driven doubly fed induction generator , 2003 .

[12]  Narayan C. Kar,et al.  Fuzzy Logic Based Vector Control of a Doubly-Fed Induction Generator in Wind Power Application , 2006 .

[13]  Jon Clare,et al.  Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation , 1996 .

[14]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  N.C. Kar,et al.  A novel PI gain scheduler for a vector controlled doubly-fed wind driven induction generator , 2005, 2005 International Conference on Electrical Machines and Systems.