Hybrid animated oat Optimization–Based maximum power point tracking for small-scale wind energy conversion systems employing a SEPIC converter

This study explores a novel hybrid optimization approach called Hybrid Animated Oat Optimization (AOO) to maximize power point tracking (MPPT) in small-scale wind energy conversion systems. This algorithm combines the search characteristics of the AOO with two additional steps, adaptive contraction and reseeding, to accelerate global power-point tracking when the wind changed rapidly. The Wind Energy Conversion System (WECS) presented in this study consists of a wind turbine model, a Permanent Magnet Synchronous Generator (PMSG), an exact three-phase rectifier, and a Single-Ended Primary Inductor Converter (SEPIC) to regulate a more constant DC-link voltage. The analysis includes four MPPT techniques: Perturbation and Observation (P&O), Fuzzy Logic Control (FLC), Particle Swarm Optimization (PSO), and Hybrid AOO, all examined in MATLAB/Simulink using the identical wind profile between 8 m/s and 12 m/s. The algorithms exhibit varying efficiencies; overall, the Hybrid AOO method produced the best results. The Hybrid AOO achieves a 97.02 % tracking efficiency and only a ripple of 15 W and a settling time of 0.12 s. Therefore, the Hybrid AOO method provides approximately 9 % higher marginal energy yield than the other tracking methods. The results of the study further proves that the high-gain SEPIC converter offers promising performance when combined with this adaptive controller, designed for a stable and cost-effective design suited for small wind energy conversion systems.