Optimization-Based Maximum Power Point Tracking for Wind Energy Systems: A Comparative Study of Gray Wolf and Whale Algorithms

Wind energy faces challenges in maintaining energy efficiency within conversion systems. Maximum power point tracking (MPPT) techniques have been employed to address these challenges. Traditional methods frequently depend on precise system parameters or fixed control structures, which may lead to performance degradation. Conversely, optimization-based algorithms can function under more favorable conditions without necessitating structures. In this study, two metaheuristic algorithms, the Gray Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA), were implemented as MPPT controllers in the MATLAB/Simulink environment. The main contribution of this study is the evaluation of two metaheuristic optimization algorithms and the provision of practical insights into their suitability for wind energy systems. The simulation results show that the GWO-based method outperforms its WOA counterpart, achieving a higher tracking efficiency of 98.2% and a lower oscillation rate (<2%), indicating higher effectiveness under dynamic wind conditions.