MPPT and Pitch Angle Based on Neural Network Control of Wind Turbine Equipped with DFIG for All Operating Wind Speed Regions

The wind turbine coupled with a doubly fed induction generator (DFIG) demonstrates good robustness and energy performances against the intermittent nature of the wind, which allows many control strategies driving this system to control it in the right conditions. Besides, the wind energy is completely depending on the wind flow which is variable and can overpass the rated value of the wind speed. In this case, the wind turbine should be sheltered versus load stress and the potential risk of destruction. In this paper, to profit from wide wind speed and exploiting the totality of wind energy, an artificial neural network is designed to implement a maximum power point tracking MPPT-pitch angle control strategy for controlling the wind turbine. This strategy lets the DFIG extract the maximum power and protects the wind turbine system from the overload of the electromagnetic torque when the wind speed is higher than its rated value. The simulation results are performed using the Matlab/Simulink environment and show well control performances under a wide wind speed range.