AN IMPROVED GRAY WOLF OPTIMIZATION-BASED HILL CLIMBING MPPT ALGORITHM TO OPERATE ENHANCEMENT OF PV SYSTEMS IN PARTIAL SHADING CONDITIONS

The paper introduces a novel hybrid maximum power point (MPP) tracking algorithm that integrates the enhanced gray wolf optimization (IGWO) technique with the hill climbing (HC) technique. This proposal aims to efficiently extract maximum power from a photovoltaic (PV) system that is subjected to a rapid variation in solar irradiance and partial shading conditions (PSCs). The proposed algorithm is a novel optimization technique that circumvents the need for recurrent trips between the GMPP and Local Maximum Power Point (LMPP), thereby ameliorating the limitations of the conventional (HC) algorithm. Diminished tracking efficiency, steady-state oscillations, and transients characterize the latter. The newly proposed MPPT algorithm was implemented through the utilization of MATLAB/SIMULINK tools. Subsequently, a comparative analysis was conducted with the results obtained from two other MPPT algorithms: the IGWO and HC MPPT algorithms. The findings substantiate the hypothesis that the IGWO-HC MPPT algorithm exhibits superior tracking capabilities under various severe weather conditions in comparison to the IGWO and HC MPPT algorithms.