Optimal Identification for Dynamic PV Cell Parameter Based on a Data-Extension-Driven Method

Abstract

Affected by environmental factors, equipment aging, operating status, etc., the parameters of photovoltaic (PV) models will deviate from the original setting parameters. In order to accurately identify the dynamic parameters of photovoltaics under the general simulation model, traditional parameter identification methods mainly use heuristic intelligent optimization algorithms for direct solution. Due to the limited data collected and the strong randomness of the algorithm, it is easy to make the identification accuracy and stability of photovoltaic parameters difficult to meet the requirements. To this end, this paper proposes an optimal identification method for PV dynamic parameters driven by data expansion. Firstly, the PV external characteristic data is fitted and generalized, which used the generalized regression neural network (GRNN). Then, the extended high-quality data can be used for dynamic parameter identification for PV cell. To confirm the performance of the proposed algorithm in this paper, this paper expands based on the actual external characteristic data of different proportions and uses the general PV simulation model to conduct comparative tests on various commonly used algorithms. The case studies under different scenarios show that the proposed algorithm can provide a more reliable and well-represented fitness function to the metaheuristic algorithms. Therefore, the optimization accuracy and stability of the proposed algorithm for dynamic PV cell parameter identification can be significantly improved simultaneously.