Parameter identification of grid-connected photovoltaic inverter based on adaptive - improved GPSO algorithm

Abstract

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 with high accuracy are obtained. The effectiveness and applicability of the identification method are verified by simulation.