A Novel Improved Puma Optimizer to Boost Photovoltaic Array Production in Partially Shaded Environments

This research proposes an improved Puma optimization algorithm (IPuma) as a novel dynamic reconfiguration tool for a photovoltaic (PV) array linked in total-cross-tied (TCT). The proposed algorithm utilizes the Newton-Raphson search rule (NRSR) to boost the exploration process, especially in search spaces with more local regions, and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma. The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems. It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions. The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms. In addition, the proposed IPuma is utilized to reconfigure a 9 × 9 PV array that operates under different shade patterns, such as lower triangular (LT), long wide (LW), and short wide (SW). In addition to other programmed approaches, such as the Whale optimization algorithm (WOA), grey wolf optimizer (GWO), Harris Hawks optimization (HHO), particle swarm optimization (PSO), gravitational search algorithm (GSA), biogeography-based optimization (BBO), sine cosine algorithm (SCA), equilibrium optimizer (EO), and original Puma, the indicated method is contrasted to the traditional configurations of TCT and Sudoku. In addition, the metrics of mismatch power loss, maximum efficiency improvement, efficiency improvement ratio, and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach. The proposed IPuma improved the generated power by 36.72%, 28.03%, and 40.97% for SW, LW, and LT, respectively, outperforming the TCT configuration. In addition, it achieved the best maximum efficiency improvement among the algorithms considered, with 26.86%, 21.89%, and 29.07% for the examined patterns. The results highlight the superiority and competence of the proposed approach in both convergence rates and stability, as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.