Parameter Estimation of Photovoltaic Cell Model Based on Improved Particle Swarm Optimization

Abstract

In the process of photovoltaic system converting solar energy into electric energy, the PV model is established. The parameters of the model have a large impact on the conversion performance and efficiency. For the purpose of accurate identification of circuit model parameters, an improved optimization algorithm based on particle swarm optimization is adopted, which combines the optimization strategy and hybrid algorithm proposed in recent years, It makes up for the fact that traditional particle swarm algorithms tend to be stuck in local optimal solutions and search efficiency decreases in multi-dimension. such as easy to fall into the local optimal solution and the decline of search efficiency in multi-dimensional. The improved algorithm is applied to the parameter identification of PV models with a single diode, and the accuracy of the algorithm is verified. The original particle swarm optimization algorithm and the optimization algorithm are simulated and the difference between the simulation data and the experimental data is calculated. The experiment shows that the Root-mean-square deviation (RMSE) of the optimized algorithm is far less than that of the original Particle swarm optimization algorithm, the measurement results of current and voltage are closer to the true values, and the parameter identification is more accurate. Satisfied the needs of practical applications.