Maximum Power Point Tracking of a Wind Turbine Based on Artificial Neural Networks and Fuzzy Logic Controllers

Abstract

In this research paper, a maximum power point tracking (MPPT) has been achieved using controllers based on artificial intelligence techniques, such as fuzzy logic (FLC), and artificial neural networks (ANN) controllers, since PI and PID classical controllers cannot give good performances in many applications that include strong nonlinearity caused by wind turbines aerodynamics, power converters of the conversion system, and the nature of wind flow. For this reason, we have proposed to use three MPPT control strategies; classical PI controller, fuzzy logic controller (FLC), and artificial neural network (ANN) controller. To avoid wind turbine catastrophes in high winds, the technique of pitch control has been investigated in parallel. Using MATLAB/Simulink, the proposed technique has been validated on a variable speed wind turbine with five-phase permanents magnets synchronous generator (PMSG) connected to a grid. The simulation results show the effectiveness of the proposed FLC and ANN controllers to achieve high tracking performance in the variable speed wind energy conversion systems (WECS).