Power enhancement using grey wolf optimizer algorithm for doubly fed induction generator based on fuzzy logic controller

The present article investigates innovative control technique for wind energy conversion systems using doubly fed induction generators, accentuating the limitations of conventional proportional-integral controllers under steady-state and variable wind conditions. It examines the integration of nonlinear fuzzy logic controllers to improve robustness, stability, and power quality. Furthermore, a fuzzy grey wolf optimizer algorithm is employed to optimally tune controller gains, optimizing both active and reactive power regulation. The research models the wind turbine, generator, machine and grid-side converters, with MATLAB/Simulink program validating the efficiency of the proposed technique. Results demonstrate that the integrated fuzzy-GWO controller significantly outperforms conventional methods. Specifically, for active power control, it reduces the Integral Time Absolute Error (ITAE) by 98.9% compared to the PI controller and by 95.8% compared to the Fuzzy PD controller. This translates to a faster response with negligible overshoot and superior tracking accuracy under both steady state and variable wind conditions, thereby improving the efficiency and reliability of wind energy systems.