Hybrid SVEA Fuzzy P&O MPPT Control for Enhanced Global Power Tracking in Partially Shaded PV Systems

Abstract Global maximum power point (GMPP) tracking under partial shading conditions (PSC) remains challenging because multiple local power maxima can restrict the effectiveness of conventional photovoltaic (PV) maximum power point tracking (MPPT) techniques. This study proposes a hybrid Sturnus vulgaris escape algorithm (SVEA) fuzzy perturb and observe (P&O) MPPT controller integrating fuzzy adaptive step-size regulation, SVEA-based exploration–exploitation, local-trap detection, and conditional rescue searching. The controller was evaluated in MATLAB using a 250 W three-section partially shaded PV module connected to a DC–DC boost converter under uniform irradiance, partial shading, and dynamic irradiance and load disturbances. Comparative evaluations included adaptive fuzzy P&O, modified coot optimization algorithm (MCOA), and improved grey wolf optimization (IGWO). Under three static irradiance conditions, the hybrid controller extracted 249.846 W, 212.985 W, and 199.802 W, respectively, achieving a mean GMPP tracking efficiency of 99.9995% and 98% power-threshold acquisition times of 0.0070–0.0072 s. Under dynamic operating conditions, the controller achieved a mean tracking efficiency of 99.9994% and extracted 497.128 J over 2.5 s, exceeding the energy extraction of adaptive fuzzy P&O, MCOA, and IGWO by 4.40%, 11.42%, and 29.25%, respectively. Parameter sensitivity analysis across 65 simulation runs yielded tracking efficiencies of 99.9985–99.9995%. The mean computational execution time was 109.61 µs, exceeding the configured 100 µs sampling interval, while switching analysis revealed greater PV-power ripple than adaptive fuzzy P&O. The findings demonstrate consistent GMPP tracking across the investigated numerical operating conditions, although computational timing, converter ripple, and experimental validation remain important considerations for practical deployment.