Application and Evaluation of a Bipolar Improvement-Based Metaheuristic Algorithm for Photovoltaic Parameter Estimation

Photovoltaic (PV) systems play a significant role in renewable energy production. Due to the nonlinear and multi-modal nature of PV models, using accurate model parameters is crucial. In recent years, metaheuristic algorithms have been utilized to estimate these parameter values. While established metaheuristics like Genetic Algorithms (GAs) incorporate mechanisms such as mutation and selection to maintain diversity, they may still encounter challenges related to premature convergence when navigating the complex, multi-modal landscapes of PV parameter estimation. In this study, the performance of the previously proposed Bipolar Improved Roosters Algorithm (BIRA), which enhances search efficiency through a bipolar movement strategy to balance exploration and exploitation phases, is evaluated. BIRA is compared with the Simple GA (SGA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) in estimating the electrical parameters of a single-diode PV model using experimental current-voltage data. The experimental results demonstrate that BIRA outperforms its competitors, achieving the lowest Root Mean Squared Error (RMSE) of 1.0504 × 10−3 for the Siemens SM55 and 4.8698 × 10−4 for the Kyocera KC200GT modules. Furthermore, statistical analysis using the Friedman test confirms BIRA’s superiority, ranking it first among all tested algorithms across both datasets. These findings indicate that BIRA is a effective and reliable tool for accurate PV parameter estimation.