Kaisong Dong / State Grid Gansu Electric Power Research Institute
Junhui Yan / Xi'an University of Technology
Weicheng Shen / State Grid Gansu Electric Power Research Institute
Shaoyu Li / State Grid Gansu Electric Power Research Institute
Xiping Ma / State Grid Gansu Electric Power Research Institute
Rong Jia / Xi'an University of Technology
Photovoltaic inverter is the most critical component of photovoltaic power generation system, which plays an important role in the dynamic characteristics of the entire power generation system. Therefore, obtaining accurate parameters of photovoltaic inverter is the basis for analyzing the impact of photovoltaic system grid-connection. In this paper, an improved genetic particle swarm optimization (GPSO) algorithm based on self-adaptability is proposed for parameter identification of common photovoltaic inverter double closed-loop control systems. In the case of light intensity mutation and temperature mutation, the inverter parameters are identified respectively, and the identification results high accuracy are obtained. The effectiveness and applicability of the identification method are verified by simulation.