Design and implementation of a fuzzy controller for wind generators performance optimisation

Actual wind power costs together with incentives and financing options for developing renewable energy facilities make wind energy source competitive with conventional generation sources and it is believed that wind energy will be the most cost effective source of electrical power in the next future. However, the wind power production diffusion involves the development of efficient control systems able to improve wind systems effectiveness. Therefore, a design methodology, able to generate an adaptive fuzzy model for maximum energy extraction from variable speed wind turbines is proposed in this paper. The fuzzy model is designed by using fuzzy clustering combined with genetic algorithms (GA) and recursive least- squares (LS) optimisation methods. Some simulation results on a doubly-fed induction generator confirmed that the proposed design methodology is able to identify a Takagi-Sugeno-Kang (TSK) fuzzy model exhibiting adaptivity, learning capability, high speed of computation and low memory occupancy.