Optimizing the wind power capture by using DTC technique based on Artificial Neural Network for a DFIG variable speed wind turbine

Abstract

This paper proposes an Artificial Neural Network (ANN) based Direct Torque Control (DTC) technique of a doubly fed induction generator (DFIG) used in the wind power generation applications. This new intelligent approach is proposed to improve the classical DTC. The main objective of this intelligent technique is to replace the conventional switching table by a voltage selector based on (ANN) in order to reduce torque and flux ripples. The maximum power point tracking (MPPT) technique is used for maximum power extraction and the pitch control is also presented to limit the generator power at its rated value. The simulations results show the performance and efficiency of the proposed control strategy. These simulations results are confirmed by using the MATLAB/Simulink environment.