Parameter Identification of Photovoltaic Cell Model Based on Enhanced Particle Swarm Optimization

Photovoltaic (PV) cell (PVC) modeling predicts the behavior of PVCs in various real-world environmental settings and their resultant current-voltage and power-voltage characteristics. Focusing on PVC parameter identification, this study presents an enhanced particle swarm optimization (EPSO) algorithmto accurately and efficiently extract optimal PVC parameters. Specifically, the EPSO algorithm optimizes the minimum mean squared error between measured and estimated data and, on this basis, extractsthe parameters of the single-, double-, and triple-diode models and the PV module. To examine its effectiveness, the proposed EPSO algorithm is compared with other swarm optimization algorithms. The effectiveness of the proposed EPSO algorithm is validated through simulation. In addition, the proposed EPSO algorithm also exhibits advantages such as an excellent optimization performance, a high parameter estimation accuracy, and a low computational complexity.

Automated summary

The study introduces an enhanced particle swarm optimization algorithm for accurately and efficiently extracting optimal parameters of photovoltaic cells. Results demonstrate that the algorithm has excellent optimization performance, high parameter estimation accuracy, and low computational complexity.