Intelligent control for large-scale variable speed variable pitch wind turbines

Large-scale wind turbine generator systems have strong nonlinear multivariable characteristics with many uncertain factors and disturbances. Automatic control is crucial for the efficiency and reliability of wind turbines. On the basis of simplified and proper model of variable speed variable pitch wind turbines, the effective wind speed is estimated using extended Kaiman filter. Intelligent control schemes proposed in the paper include two loops which operate in synchronism with each other. At below-rated wind speed, the inner loop adopts adaptive fuzzy control based on variable universe for generator torque regulation to realize maximum wind energy capture. At above-rated wind speed, a controller based on least square support vector machine is proposed to adjust pitch angle and keep rated output power. The simulation shows the effectiveness of the intelligent control.

[1]  Rolf Hoffmann A comparison of control concepts for wind turbines in terms of energy capture , 2002 .

[2]  L.G. Franquelo,et al.  Improving transition between power optimization and power limitation of variable speed, variable pitch wind turbines using fuzzy control techniques , 2000, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies.

[3]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[4]  N. D. Hatziargyriou,et al.  A new control scheme for variable speed wind turbines using neural networks , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[5]  R. Chedid,et al.  Intelligent control of a class of wind energy conversion systems , 1999 .

[6]  Xin Ma,et al.  Adaptive Extremum Control and Wind Turbine Control , 1997 .

[7]  Y. D. Song,et al.  Control of wind turbines using nonlinear adaptive field excitation algorithms , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[8]  M. McCormick,et al.  A fuzzy logic controlled power electronic system for variable speed wind energy conversion systems , 2000 .

[9]  E. Lee,et al.  Variable universe stable adaptive fuzzy control of a nonlinear system , 2002 .

[10]  Hongxing Li,et al.  Variable universe stable adaptive fuzzy control of nonlinear system , 2002 .

[11]  R. W. De Doncker,et al.  Doubly fed induction generator systems for wind turbines , 2002 .

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[13]  Siegfried Heier,et al.  Grid Integration of Wind Energy Conversion Systems , 1998 .