Fuzzy-Supervised INC–ACO–GWO Switching for MPPT of a Photovoltaic System Under Dynamic Irradiance and Partial Shading

Abstract

Maximum power point tracking (MPPT) is essential for maximizing energy extraction from photovoltaic (PV) systems under varying irradiance, temperature, and partial-shading conditions. This simulation-based quantitative study presents a fuzzy-logic-supervised hybrid MPPT controller that dynamically switches among Incremental Conductance (INC), Ant Colony Optimization (ACO), and Grey Wolf Optimization (GWO). The controller was implemented in MATLAB/Simulink using a 50-W PV module and a boost converter and compared with standalone INC, ACO, and GWO under dynamic irradiance, temperature variation, and the specified partial-shading condition. Performance metrics included MPPT efficiency, settling time, steady-state oscillation index, tracking error, and simulated energy harvested over the test interval. The proposed controller achieved a reported mean MPPT efficiency of 99.5% (versus 97.8% for INC, 98.2% for ACO, and 98.9% for GWO) and the lowest oscillation index of 0.18% (versus 3.2%, 1.8%, and 1.5%, respectively). Under the specified partial-shading condition it reached 99.46% tracking efficiency. Over the 40-s combined irradiance–temperature simulation, it produced a reported 3.1% increase in simulated energy harvested relative to INC and 1.6% relative to GWO. Although INC recorded the shortest settling time (0.14 s), the proposed controller settled in 0.18 s while providing higher reported efficiency and lower oscillation. These findings are limited to the simulated scenarios; experimental, hardware-in-the-loop, repeated stochastic-run, and computational-cost validation were not established in the supplied study.