Mathematical Modeling and Parameter Estimation of Solar Cell Using Particle Swarm Optimization

Renewable energy sources such as solar are becoming increasingly popular throughout the world. The structure of PV cells is demonstrated using a single-diode configuration. This paper presents the mathematical derivation of the single-diode configuration of the PV cell and models. In addition, the single diode configuration was modeled using the MATLAB Simulink model. The current–voltage and power-voltage waveforms were selected using a PV model at various temperatures and irradiance levels to explore the impact of temperature and irradiance on voltage and current waveforms. Then, assume that my PV cell had five parameters that were not given. Particle swarm optimization (PSO) techniques were used to identify this unknown parameter. The PSO's performance was compared to those of genetic algorithms (GAs), Villalva's Method, Accarino's Method, Iterative Method, and Silva's Method for single diode models of Kyocera (KC200GT). Instead of using a gradient-based approach, the PSO method utilizes a wide range of values for each parameter to generate the parameters of solar cells, allowing it to get as near as feasible to the parameters that are used in actual solar cells. This is feasible even without a reliable initial prediction. According to the observations of both simulated and real current–voltage data, the proposed method is more accurate and faster than the one previously utilized. Comparative research with various algorithms also helped us validate our observations.