Self tuning control of wind turbine using neural network identifier

The nonlinear characteristics of the wind turbines and electric generators necessitate that grid connected wind energy conversion systems (WECS) use nonlinear controls. The present paper proposes an adaptive self tuning control strategy with neural network Morlet wavelet for WECS control. The proposed strategy is based on single layer feedforward neural networks with hidden nodes of adaptive Morlet wavelet functions controller and an infinite impulse response recurrent structure. The neuro controller is based on a certain model structure to approximately identify the system dynamics of WECS, and control its response. The proposed controller is studied in three situations: without noise, with measurement input noise and with disturbance output noise. Finally, the results of the performance of the new controller were compared with a multilayer perceptron network proving a more precise modeling and control of WECS.