Extracting photovoltaic model parameters: performance evaluation of the Puma Optimizer Algorithm (POA) across single, double, and triple diode models

Photovoltaic (PV) energy plays a vital role in the global transition toward sustainable and clean energy sources. Accurate modelling of PV systems is essential for optimising their performance, enabling effective monitoring, control, and fault diagnosis. However, the identification of model parameters remains a significant challenge. In this study, we propose a novel metaheuristic algorithm, the Puma Optimizer Algorithm (POA), to address these challenges and estimate the optimal parameters of three PV models: the Single Diode Model (SDM), the Double Diode Model (DDM), and the Triple Diode Model (TDM). The effectiveness of the POA was validated using two photovoltaic generators: the RTC France cell and the Photowatt PWP201 module. The results highlight the algorithm’s high accuracy, achieving remarkably low root mean square error (RMSE) values of 7.417921e-04 A for the RTC France cell and 1.208314e-03 A for the PWP201 module using TDM. Comparative results against six benchmark algorithms validate the superior accuracy of POA, highlighting its potential as a significant contribution to photovoltaic parameter estimation methodologies.