Adaptive Metaheuristic Optimization and Numerical Modeling for Robust Control of DFIG Wind Turbines Under Stochastic Wind and Grid Disturbances

Abstract

Reliable integration of wind energy into modern power grids requires control strategies capable of maintaining stable operation under stochastic wind conditions and grid-side disturbances. This paper presents an adaptive metaheuristic optimization and numerical modeling framework for robust multi-scenario tuning of proportional–integral controller parameters in a doubly fed induction generator (DFIG)-based wind-energy conversion system. The optimized control loops include the rotor-side converter, grid-side converter, rotor-speed loop, and DC-link voltage loop. Unlike conventional tuning approaches that rely on nominal operating points or limited deterministic cases, the proposed formulation evaluates each candidate controller over multiple operating scenarios, including start-up dynamics, step wind-speed variation, random wind fluctuation, and grid-voltage dip conditions. An Adaptive Whale Optimization Algorithm (AWOA) is developed by incorporating diversity-aware adaptation and stagnation-handling mechanisms into the standard WOA structure to improve the exploration–exploitation balance during the search process. The tuning objective combines aggregate transient-performance minimization with robustness-oriented scenario evaluation, thereby promoting controller gains that remain effective across uncertain operating conditions. Comparative numerical simulations against Grey Wolf Optimizer, Generalized Grey Wolf Optimizer, Moth-Flame Optimizer, and standard WOA show that the proposed AWOA achieves lower aggregate Integral Time Squared Error values across the considered cases. Convergence assessment, ablation analysis, and hold-out robustness testing further confirm the contribution of the adaptive mechanisms. Time-domain responses also demonstrate improved DC-link voltage regulation and reactive-power recovery under severe grid disturbances. These results indicate that the proposed framework can enhance the reliability and resilience of grid-connected DFIG wind-energy systems, supporting more robust and sustainable renewable-energy integration.