PV Model Parameter Estimation Based on Hybrid Metaheuristic Approach

Estimating PV cell model parameters using actual current-voltage data is crucial in determining the functionality of PV systems. Obtaining PV cell model parameters with the help of metaheuristic algorithms is a feasible solution. In this study, single and double-diode PV cell model parameters are obtained using the Salp swarm algorithm (SSA), arithmetic optimization algorithm (AOA), and hybrid SSA-AOA. Polycrystalline TRP-050B PV module current-voltage data are used to improve the applicability and accuracy of the proposed method. The current-voltage data of the PV module were obtained at 27°C and 200 W/m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> irradiance values. The statistical functions of the sum of squares of errors (SSE) and root mean square of the sum of squares of errors (RMSE) were used to evaluate the functionality of the obtained results. According to the obtained results, the recommended hybrid SSA-AOA is demonstrated to be feasible, robust, and realistic.