An Effective Method for Extracting PV Model Parameters Utilizing the Red-Tailed Hawk Optimization Algorithm

The growing focus on environmental conservation underscores the urgency of research and development in clean energy. Photovoltaic (PV) systems are pivotal in the conversion of solar energy into electricity. Key parameters in PV models fluctuate with environmental temperature and illumination, influencing system performance. Precisely extracting these unknown parameters aids engineers in optimizing PV systems. This paper introduces a novel method for PV model parameter extraction using the red-tailed hawk optimization algorithm (RTH). It transforms the complex parameters extraction task into an optimization problem by minimizing the root mean square error (RMSE) between measured and calculated PV model currents. Subsequently, the RTH algorithm is employed to solve this optimization problem and search for a set of high-quality unknown parameter values. To assess the performance of the proposed method based on the RTH, the method is applied to three distinct PV models. Five metaheuristic algorithms are selected as competitors in controlled experiments to validate the proposed method’s performance. Experimental results indicate that the proposed method can calculate a superior set of parameter values, reflected in its ability to achieve smaller RMSE. Furthermore, the proposed method demonstrates significant advantages in terms of convergence speed and robustness.