Optimized Reconfiguration Of PV Arrays Under Partial Shading Using The Snake Algorithm

Abstract

The performance of photovoltaic (PV) systems is significantly diminished under partial shading due to uneven sunlight exposure, leading to considerable energy losses. While many fixed and adaptive reconfiguration methods have been investigated, environmental factors such as irregular sunlight, snow, frost, and dust further complicate optimal energy harvesting. This research introduces an innovative reconfiguration strategy that utilizes the Snake Optimizer (SO) algorithm to improve PV array performance under partial shading conditions. The proposed SO-based method specifically aims to minimize current imbalances across array rows while maximizing overall power output.  The four shading scenarios were short wide (SW), long wide (LW), short narrow (SN), and long narrow (LN).Through MATLAB-Simulink simulations of the proposed method, several standard algorithms including: Total Cross Tied (TCT), Butterfly Optimization Algorithm (BOA), Harris Hawks Optimization (HHO) and Flower Pollination Algorithm (FPA) were compared also show the proposed algorithm to be effective.PV power output had significant increases: 37.5% shorter wide, 22.7% long wide, 19.7% shorter narrow and 12.9% long narrow.Without a doubt these results offer insight into the potential of the SO algorithm to provide an efficient and reliable way for increasing PV energy harvests in real-world environments where shading occurs.