A photovoltaic parameter identification method based on Pontogammarus maeoticus swarm optimization

Abstract

Currently, the improvement of model parameter extraction accuracy is essential to research photovoltaic (PV) fields. In this study, a model parameter identification based on Pontogammarus maeoticus swarm optimization (PMSO) is proposed. The PMSO is used for parameter identification of mathematical models for PV modules. In the PMSO algorithm, by giving the ability of free exploration to particles that are far away from the optimal solution, the search scope is expanded to avoid falling into the local optimum. Besides, the local search for each Gammarus has a better convergence for PV parameter identification. Therefore, the accuracy of parameter identification for modeling PV modules is improved. The feasibility and superiority of the proposed method are verified by measured I-V characteristics of the PV array. The experimental results and error analysis verify that when compared with the conventional meta-heuristic algorithms, the proposed method achieves higher modeling accuracy. The proposed PMSO algorithm is suitable for engineering application of parameter identification and modeling of PV modules.