COMPARISON BETWEEN SEVERAL METHODS OF IDENTIFICATION OF PHOTOVOLTAIC PARAMETERS (REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, 2025)
Renewable energy, especially photovoltaic, is a promising solution to address the depletion of fossil fuels. Using single- and dual-diode models, solar cell modeling enables prediction of photovoltaic module behavior under various environmental conditions, making accurate parameter extraction essential. In this paper, we review different methods for identifying photovoltaic parameters, such as nonlinear least squares, and several meta-heuristic algorithms such as the  genetic algorithm (GA), particle swarm optimization (PSO) and their versions combined with explicit equations (PSOX, GAX), and ant colony optimization in the continuous domain (ACOR). Experimental data are compared with these methods to identify the characteristics of a model photovoltaic panel, SY-M80W.The techniques focus on the single diode model to determine five unknown parameters, namely photovoltaic currents (Iph), saturation currents (Is), series resistance (Rs), parallel resistance (Rsh), and ideality factor (A). This comparative analysis aims to identify the most accurate method for photovoltaic parameter extraction, supporting enhanced solar energy system performance.
Keywords: Identification; Artificial intelligence (AI); Photovoltaic (PV); Meta-heuristic; Least squares; Particle swarm; Genetic algorithm