Enhanced whale optimization algorithm for maximum power point tracking of variable-speed wind generators

Abstract This paper proposes an enhancement of the meta-heuristic whale optimization algorithm (WOA) for maximum power point tracking (MPPT) of variable-speed wind generators. First of all, twenty-three benchmark functions tested the enhanced whale optimization algorithm (EWOA). The statistical results of EWOA compared with the results of other algorithms (WOA, salp swarm algorithm (SSA), enhanced SSA (ESSA), grey wolf optimizer (GWO), augmented GWO (AGWO), and particle swarm optimization (PSO). The non-parametric statistical test and convergence curves proved the superiority and the speed of the EWOA Secondly, the EWOA and WOA are implemented to design optimal Takagi-Sugeno fuzzy logic controllers (FLCs) to enhance the MPPT control of variable-speed wind generators. Real wind speed data has confirmed the robustness of optimal EWOA-MPPT. The simulation results revealed that EWOA is a promising algorithm to be applied for solving different engineering problems.