Elevating PV model performance: Accurate and reliable parameter extraction of solar cell models with state-of-art metaheuristic algorithms

This study focuses on parameter identification of various solar cell (SC) and PV module configurations, including the single diode SC, double diode SC, STP6-120/36, STM6-40/36, and Photowatt-PWP201. In this direction, seven state-of-the-art metaheuristic algorithms, including dynamic fitness-distance balance-based LSHADE (dFDB-LSHADE), nonlinear marine predator algorithm (NMPA), hippopotamus optimization (HO), marine predators algorithm (MPA), walrus optimizer (WO), exponential distribution optimizer (EDO), and manta-ray foraging optimization (MRFO) are employed to extract the unknown model parameters based on voltage-current measurement data. The optimum configuration of the SC parameters is identified by minimizing the root mean square error (RMSE) between the simulated and measured cell currents. The effectiveness of the algorithms was tested through extensive experimentation, incorporating statistical analysis, convergence analysis, box plots, and model validation. The optimization findings show that the dFDB-LSHADE produced the lowest RMSE and the most accurate predictions for all SC models. The box plots and statistical metric results clearly demonstrate that dFDB-LSHADE is a robust and reliable method for the SC parameter identification problem.