Revolutionizing photovoltaic power: An enhanced Grey Wolf Optimizer for ultra-efficient MPPT under partial shading conditions

Abstract

This paper presents an Enhanced Grey Wolf Optimizer (E-GWO) algorithm for Maximum Power Point Tracking (MPPT) in photovoltaic (PV) systems under partial shading conditions. The proposed E-GWO introduces a novel parameter minimization strategy for the convergence factor $\omega$, enabling rapid and precise tracking of the global maximum power point (GMPP) without overshoot. Key improvements to the standard GWO framework enhance tracking accuracy, stability, and overall system performance.

The proposed MPPT approach is validated through extensive simulations and real-world experiments implemented on a dual-core DSP LAUNCHXL-F28379D using MATLAB/Simulink. Experimental results demonstrate that E-GWO reduces tracking time by up to 99.90% compared to traditional GWO methods while increasing dynamic tracking efficiency by over 9%. Furthermore, the E-GWO consistently outperforms conventional GWO variants and other swarm-based algorithms, ensuring superior power output in diverse shading scenarios.