Multi-strategy improved partial reinforcement optimization algorithm for accurate photovoltaic parameter extraction

Accurate parameter identification for photovoltaic (PV) cells is essential for the evaluation, control, and improvement of PV systems. To address this, various metaheuristic algorithms have been widely employed. Most of the methods and techniques found in the existing literature rely on Root Mean Square Error (RMSE) values to validate the performance of the approaches employed. The objective of this study is to propose a modified RMSE calculation formula, providing a more precise evaluation metric for optimization algorithms used to extract PV model parameters. The RMSE values of the RTC France solar cell is first calculated using 30 distinct algorithms. Next, a multi-strategy improved partial reinforcement optimization algorithm (IAF-PRO) is developed, which incorporates an improved nonlinear selection rate and adaptive strong and weak stimulation factors. This modification enhances the algorithm’s convergence speed and precision. Subsequently, the collaboration of mirror reflection boundary handling mechanism and fast random opposition-based learning synergistically promotes population diversity and assists the algorithm in avoiding local optima. Finally, the suggested IAF-PRO is employed to estimate parameters for various PV models, including single diode, double diode, and PV module. Experimental results show that IAF-PRO consistently achieves the lowest RMSE values across all five PV models compared to nine state-of-the-art algorithms, including PSO and GWO, establishing its efficacy for PV parameter estimation.