This study addresses the identification and optimization of parameters in the single-diode photovoltaic (PV) model (SDM), a widely adopted approach for simulating the electrical behavior of PV modules. The five key parameters, namely, the photo-generated current ( I ph ), the reverse saturation current ( I 0 ), the series resistance ( R s ), the shunt resistance ( R sh ), and the diode ideality factor (n), were initially estimated under Standard Test Conditions (STC) using manufacturer-provided data and the PV System toolbox in MATLAB. Based on these initial values, simulations of the current–voltage (I–V) and power–voltage (P–V) characteristics were conducted under real operating conditions using the Lambert W function and the Newton–Raphson method. An optimization procedure was then applied, combining the Newton–Raphson technique with two optimization algorithms: the Genetic Algorithm (GA) and the Levenberg–Marquardt (LM) method. The performance of each method was evaluated using three statistical indicators: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination ( R 2 ). Among the tested approaches, the Genetic Algorithm achieved the highest accuracy, with an RMSE of 0.0359 A, a MAE of 0.0270 A, and an R 2 value of 0.9993. Finally, the analysis of environmental influence confirmed the significant impact of temperature and irradiance on module performance, particularly on the open-circuit voltage, maximum power output, and overall energy generation.
Mabrouk et al. (Wed,) studied this question.