Efficient Parameter Extraction for Accurate Modeling of Solar Photovoltaic Cell using Arctic Puffin Optimization

Accurate determination of photovoltaic (PV) model parameters is essential for studying the variables that affect PV power generation efficiency. The extraction of parameters from the PV model presents a challenging problem because of its multi-model as well as nonlinear characteristics. This paper presents a solution to this challenge through the use of the Arctic Puffin Optimization (APO) algorithm. The parameter identification technique has been described as an optimization problem focused on minimizing the current-based root mean squared error (RMSE). Additionally, the APO algorithm is adopted for single, double as well as triple diode model parameter extraction for the RTC France solar PV module. The outcomes indicate that the APO algorithm combined with the Newton-Raphson method demonstrates superior robustness and convergence accuracy contrasted to the other meta-heuristics (MH) algorithm with obtained minimum RMSE value. Additionally, an extensive performance evaluation of the MH algorithms has been performed, utilizing convergence curves, boxplots, as well as characteristic curves for current versus voltage (I-V) and power versus voltage (P-V), derived from the experimental outcomes. Furthermore, the APO algorithm demonstrates superior results in comparison with other MH algorthims and acts as a potential tool for efficient parameter extraction of the solar PV Model from experimental data.