The present work investigates the modeling and parameter identification of solar photovoltaic systems using a hybrid optimization framework that integrates Genetic Algorithm and Particle Swarm Optimization methods. Solar photovoltaic systems are critical in the search for renewable energy sources, and their performances are highly dependent on environmental conditions like temperature, irradiance, and partial shading. The development of an accurate model is vital to ensure the efficient design and optimal performance of such systems. Conventional techniques of estimating parameters are not effective in handling nonlinearities and sensitivity associated with PV systems. The hybrid GA-PSO algorithm combines the global search capability of the Genetic Algorithm with fast convergence properties of Particle Swarm Optimization to conduct an efficient optimization of key parameters of the PV system, including photocurrent, series resistance, shunt resistance, and the diode ideality factor. The key focus of this paper was aimed at the enhancement of accuracy in the estimation of solar PV systems' parameters under varying environmental conditions, thus leading to better PV performance and efficiency. The research methodology involved simulating the solar PV system using MATLAB, optimizing key parameters using a hybrid GA-PSO algorithm, and model validation with experimental data. Optimized parameters are further utilized to develop current-voltage (I-V) and power-voltage (P-V) characteristics for different conditions of irradiance and temperature.
Chimeuche et al. (Thu,) studied this question.