Parameter Identification of Single-Diode Solar Photovoltaic Model Using Particle Swarm Optimization Hybrid with Newton-Raphson Method

To model and simulate photovoltaic (PV) systems, it is necessary to understand the equivalent circuit parameters for PV cells. They are crucial for monitoring and controlling the actual operation of simulating PV modules and predicting their power generation. There have been numerous approaches proposed for determining the optimal parameters. This study aims to extract the parameters using a single-diode PV cell variational I–V curve at maximum power, open circuit voltage, and short circuit current using particle swarm optimization (PSO) hybrid with the Newton-Raphson method in a MATLAB script. The proposed method provides an accurate estimation of the roots of the nonlinear characteristics of PV cells through numerical analysis because of the absence of mathematical simplifications and physical assumptions. The Newton-Raphson method is best for solving for the ideal values of the solar cell parameters iteratively, and the PSO algorithm is very suited to finding the optimal initial values of the parameters that can use as input for the Newton-Raphson method. Statistical error tests on some PV modules, such as the root mean square error (RMSE), were used to evaluate the accuracy, validity, and comparability of the model’s predictions. The system responds effectively to changes in cell parameters, which results in good agreement between the measured and predicted data. Additionally, the results supported the idea that as solar radiation rises, harvested power output falls, while temperatures rise. Generally, the simulation results show that the suggested method is simple, fast, and low in computational complexity.