Enhancement of MPPT Control in PV-Wind Hybrid Systems Using a Hybrid HWOA-GWO Optimization Algorithm
DOI:
https://doi.org/10.12928/biste.v8i5.17012Keywords:
Hybrid PV-Wind System, Maximum Power Point Tracking, Grey Wolf Optimization, Whale Optimization Algorithm, Event-Triggered ControlAbstract
Hybrid photovoltaic (PV)-wind systems require maximum power point tracking (MPPT) controllers that remain effective under source intermittency, shared DC-link dynamics, battery constraints, and measurement noise. This research proposes an event-triggered hybrid HWOA-GWO MPPT framework in which a Grey Wolf Optimizer performs population-based global exploration and a whale-inspired spiral operator refines elite candidates, while a filtered adaptive perturb-and-observe tracker maintains the operating point between major regime changes. The research contribution is a converter-aware duty-cycle formulation that embeds MPPT decisions within a coupled PV-wind-battery DC microgrid rather than evaluating the PV source in isolation. MATLAB co-simulation is used to assess tracking efficiency, PV power ripple, DC-link behavior, battery power, state of charge, and settling behavior under four irradiance regimes and measurement noise. Across the four regimes, the mean tracking efficiency increases from 98.177 ± 0.100% for noisy adaptive P&O to 99.386 ± 0.047% for the proposed method. Mean PV power ripple decreases from 13.073 ± 2.249% to 6.523 ± 1.541%, corresponding to an approximately 50.1% reduction, while mean settling times remain comparable. The DC-link response remains bounded and the battery operates within the imposed saturation logic. The results indicate that event-triggered global repositioning combined with low-cost local tracking improves energy capture and ripple suppression without claiming unsupported CPU-time performance.
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