Comparative Study of Metaheuristic Algorithms Applied to PI and FOPI Controllers for DFIG-Based Wind Turbine Systems

Abstract

With the objective of improving dynamic response and enhancing power extraction, this work proposes a metaheuristic-based design of a Fractional-Order Proportional-Integral (FOPI) regulator for a Doubly-Fed Induction Generator (DFIG). A two-stage offline tuning strategy based on four metaheuristic algorithms, namely Grey Wolf Optimization (GWO), Equilibrium Optimizer (EO), Particle Swarm Optimization (PSO), and Harris Hawk Optimization (HHO), was employed to optimize the FOPI parameters. The algorithms were comparatively evaluated, and GWO emerged as the best-performing optimization technique. Subsequently, a comparative analysis between the conventional PI and the optimized FOPI controllers were conducted, focusing on dynamic response. The results demonstrated the superior performance of the FOPI controller across multiple performance indicators, with improvements ranging from 2% to 40% in overshoot, settling time, and steady-state error. These enhancements were further supported by the Integral Absolute Error (IAE) metric, where the FOPI controller yields lower errors for   (2.9978 versus 3.0237),  (1.5456 versus 2.6367), and  (4.0076 versus 6.7130). In addition, the optimized FOPI controller improved the reactive power behaviour by reducing rotor-side oscillations and achieved a 17% reduction in stator-side reactive power. These findings validate the effectiveness of the proposed two-stage optimized FOPI control strategy in improving dynamic response, reducing control errors, and enhancing the overall stability and performance of DFIG-based wind energy conversion systems