Photovoltaic Cell Model Parameter Extraction Using Electric Eel Feeding Optimizer

Temporarily accepted

Solar energy has become a key solution in the global transition to renewable energy sources, driven by environmental and climate change concerns. This is largely due to its purity, availability, and cost-effectiveness. Accurate evaluation of hidden factors in photovoltaic (PV) models is crucial for the efficient exploitation of the potential of these systems. In this study, a novel parameter estimation approach is applied, using the electric eel feeding optimizer (EEFO), recently documented in the literature, to address such engineering problems. EEFO emerges as a competitive metaheuristic methodology that plays a key role in enabling precise parameter extraction. To maintain scientific integrity and fairness, the study uses the RTC France solar cell as a reference case. We incorporate the EEFO approach together with the Newton-Raphson method into the parameter tuning process for three PV models: single-diode, dual-diode, and tri-diode models, using a common experimental framework. We selected the RTC France solar cell for single, dual and triple diode models due to its significant role in the field. It serves as a reliable evaluation platform for the EEFO approach. We perform a thorough evaluation using statistical, convergence and time-lapse studies, showing that EEFO consistently achieves low RMSE values. This indicates that EEFO is able to accurately estimate voltage-current characteristics. The smooth convergence behavior of the system further enhances its effectiveness. Comparison of EEFO with competing methodologies reinforces its competitive advantage in optimizing PV model parameters, showing its potential to significantly increase solar energy utilization.