An Levenberg-Marquardt trained feed-forward back-propagation based intelligent pitch angle controller for wind generation system

The frequent variation in wind speed affects the wind turbine (WT) to produce fluctuating output power and this may negatively collide the entire power system. This paper proposes a Feed Forward Back Propagation Neural Network (FFBP-NN) based pitch angle controller to mitigate the output power fluctuation in a grid connected wind generation system. The outstanding aspect of the proposed controller is that the optimal power of the WT is tracked in such a way that the output power is smoothed, when the wind speed flows below rated speed. Consequently, during above rated speed; the power is smoothed by traditional power regulating method. Further, the FFBP-NN controller is trained online using Levenberg-Marquardt (LM) algorithm and connecting weights of the neurons are updated means of LM algorithm using back propagation methodology. The effectiveness of the proposed FFBP based pitch controller is analyzed through the simulation study carried out in MATLAB/Simulink environment.