Abstract
Accurately simulating photovoltaic (PV) modules requires precise parameter extraction, a complex task due to the nonlinear nature of these systems. This study introduces the Mother Tree Optimization with Climate Change (MTO-CL) algorithm to address this challenge by enhancing parameter estimation for a solar PV three-diode model. MTO-CL improves optimization performance by incorporating climate change-inspired adaptations, which affect two key phases: elimination (refreshing 20% of suboptimal solutions) and distortion (slight adjustments to 80% of remaining solutions). This balance between exploration and exploitation allows the algorithm to dynamically and effectively identify optimal parameters. Compared to seven alternative methods, MTO-CL shows superior performance in parameter estimation for various solar modules, including ST40 and SM55, across different irradiances and temperatures. It achieves exceptionally low Root Mean Square Error (RMSE) values from 0.0025A to 0.0165A and Mean Squared Error (MSE) values between 6.2 × 10^-6 and 2.7 × 10^-4, while also significantly minimizing power errors, ranging from 22.86 mW to 239.40 mW. These results demonstrate MTO-CL's effectiveness in improving the accuracy and reliability of PV system modeling, offering a robust tool for enhanced solar energy applications.