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Solar energy has emerged as a key solution in the global transition to renewable energy sources, driven by environmental concerns and climate change. This is largely due to its cleanliness, availability, and cost-effectiveness. The precise assessment of hidden factors within photovoltaic (PV) models is critical for effectively exploiting the potential of these systems. This study employs a novel approach to parameter estimation, utilizing the electric eel foraging optimizer (EEFO), recently documented in the literature, to address such engineering issues. The EEFO emerges as a competitive metaheuristic methodology that plays a crucial role in enabling precise parameter extraction. In order to maintain scientific integrity and fairness, the study utilizes the RTC France solar cell as a benchmark case. We incorporate the EEFO approach, together with Newton-Raphson method, into the parameter tuning process for three PV models: single-diode, double-diode, and three-diode models, using a common experimental framework. We selected the RTC France solar cell for the single-diode, double-diode, and three-diode models because of its significant role in the field. It serves as a reliable evaluation platform for the EEFO approach. We conduct a thorough evaluation using statistical, convergence, and elapsed time studies, demonstrating that EEFO consistently achieves low RMSE values. This indicates that EEFO is capable of accurately estimating the current-voltage characteristics. The system’s smooth convergence behavior further reinforces its efficacy. Comparing the EEFO with competing methodologies reinforces its competitive advantage in optimizing solar PV model parameters, showcasing its potential to greatly enhance the usage of solar energy. |
