LUO J, SUN Y, JIANG L J. Parameter identification of photovoltaic inverter based on improved particle swarm optimization algorithm[J]. Journal of Henan Polytechnic University(Natural Science) , 2025, 44(1): 124-133.

doi: 10.16186/j.cnki.1673-9787.2023010028

Received: 2023/01/18

Revised: 2023/08/06

Published: 2025/01/02

Parameter identification of photovoltaic inverter based on improved particle swarm optimization algorithm

LUO Jian1, SUN Yue2,JIANG Lijuan1

1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology， Chongqing University， Chongqing  400044， China; 2. Training Center， State Grid Anhui Electric Power Co.， Ltd.， Hefei  2300,Anhui， China

Abstract: Accurate grid-connected photovoltaic（PV） inverter model is an important tool to study the fault characteristics of power system under large-scale PV access. Objectives In order to solve the problem that the characteristics of the existing PV inverter simulation model are quite different from those of the actual PV inverter， Methods this paper presented the method of parameter identification to construct the identification model of inverter. Taking a 1MW photovoltaic power station in Yunyang， Chongqing as the actual reference model， the working interval of the inverter was divided into three stages according to the actual working conditions， and the high and low sensitivity levels of the parameters to be identified in the three stages were divided by mathematical disturbance method. Then， the actual working data of photovoltaic power station was collected in stages， and the initial value range of each parameter to be identified was obtained after the data was analyzed and processed， and the synchronous identification parameter experiment was designed as a reference. Finally， an improved chaos genetic algorithm of particle swarm optimization（CGAPSO） was proposed as an identification algorithm to identify the relevant parameters step by step. The advantages of the proposed method can be obtained by comparing the synchronous identification results of the parameters， and the identification results were substituted into the simulation model. Results The error of synchronous identification results of low sensitivity parameters was far beyond the acceptable range， and the error of relevant parameters identified by CGAPSO step by step was less than 1.1%， which was much higher than the accuracy of synchronous identification results. Conclusions The output data of the identification model based on the improved particle swarm optimization algorithm had a high agreement with the actual inverter working data，which could accurately reflect the actual working characteristics of the inverter.

Key words: photovoltaic grid-connected inverter; inverter control strategy; identification of parameter; mathe-matical perturbation method; improved particle swarm optimization