Maximum power extraction of partially shaded solar photovoltaic array using reconfiguration method

This article presents a reconfiguration strategy, named the Cheetah Optimization Algorithm (COA), for maximizing power generation in partially shaded solar photovoltaic (PSPV) arrays. The proposed method's originality aims to mitigate the negative impacts of partial shading, improve the output power of the PV system, and prevent thermal failure of shaded solar panels. The strategy combines efficient maximum power point tracking techniques with suitable photovoltaic system design topologies. The COA is done on the MATLAB platform and is analyzed with the existing methods such as the Wild Horse Optimizer (WHO), Salp Swarm Algorithm and Heap-based Optimizer. The outcome shows the superior performance of the COA in achieving the extraction of maximum power compared to the other methods. Additionally, by comparing the two patterns of partial shading, it is shown that the COA extracts more power than the existing methods and gets to the global power value faster. The COA is compared with existing methods, including WHO, SSA, and HBO. In terms of accuracy, the proposed system achieves the highest value of 0.93, surpassing the accuracy of HBO, WHO, and SSA, which stand at 0.8, 0.9, and 0.9, respectively. The specificity, precision and recall are also reflect the superiority of the proposed system with the values of 0.87, 0.9, and 0.86, respectively, outperforming the existing algorithms in most categories.