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In order to optimize the performance of a Solar Photovoltaic (PV) system, it is necessary to develop an appropriate PV cell model and accurately determine the unknown parameters associated with the model. The process of extracting parameters for PV models is a complex optimization issue that involves nonlinearity and multiple models. Accurate estimation of the characteristics of PV units is crucial since these factors significantly affect the performance of PV systems in terms of power and current generation. Consequently, this research presents an advanced methodology, known as the Pelican Optimization Algorithm (POA), aimed to find the optimal values for the unspecified parameters of PV units. In this study, the Single Diode Model (SDM) is employed to analyze four datasets like RTC France, Photowatt-PWP201, STP-120/36, as well as STM6-40/36 PV panels. The POA algorithm is utilized to determine the unknown parameters of solar PV modules. Furthermore, to enhance the precision of the obtained solutions, this study incorporates the Newton–Raphson (NR) method into the POA algorithm. The POA achieves the optimum Root Mean Square Error (RMSE) values for the four PV models (RTC France, Photowatt-PWP201, STM6-40/36 and STP6-120/36) and the values are found to be 7.7298E-04, 2.0528E-03, 1.7220E-03 and 1.4458E-02 respectively. From the results, it is observed that, POA exhibit superior performance compared to the other MH optimization algorithms. Furthermore, the statistical findings show that the POA algorithm has a higher average robustness and accuracy than the other algorithms.
Rathod et al. (Tue,) studied this question.