引用本文:程 杉,蔡子威,张旭军,等.基于雪消融算法的光伏并网逆变器低电压穿越模型多阶段参数辨识方法[J].电力系统保护与控制,2025,53(05):47-58.
CHENG Shan,CAI Ziwei,ZHANG Xujun,et al.A multi-stage parameter identification method for low-voltage ride-through model of grid-connected PV inverters based on a snow ablation algorithm[J].Power System Protection and Control,2025,53(05):47-58
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基于雪消融算法的光伏并网逆变器低电压穿越模型多阶段参数辨识方法
程 杉,蔡子威,张旭军,等
1.三峡大学电气与新能源学院，湖北 宜昌 443002；2.国网甘肃省电力公司电力科学研究院， 甘肃 兰州 730070；3.新能源电力系统国家重点实验室(华北电力大学)，北京 102206
摘要:
为获得准确的光伏逆变器低电压穿越(low-voltage-ride-through, LVRT)模型参数以满足光伏并网系统安全可靠的要求，提出了基于雪消融算法(snow-ablation-optimizer, SAO)的光伏并网逆变器低电压穿越模型多阶段参数辨识方法。首先，基于光伏发电系统低电压穿越输出曲线特性，建立了光伏低电压穿越控制数学模型并分析故障暂态过程，明确了低电压穿越过程的核心控制参数。其次，针对内环PI控制参数与LVRT的耦合性和相关性问题，提出多阶段辨识策略。最后，依据实际工程参数对光伏逆变器建模，利用雪消融算法对内环控制参数与LVRT参数进行辨识，仿真算例表明了所提辨识方法的有效性。
关键词:  光伏并网逆变器  低电压穿越  相关性分析  雪消融算法  参数辨识
DOI：10.19783/j.cnki.pspc.240457
分类号:
基金项目:宁夏自然科学基金项目资助(2023AAC03857)；新能源电力系统全国重点实验室2024年开放课题资助(LAPS24006)
A multi-stage parameter identification method for low-voltage ride-through model of grid-connected PV inverters based on a snow ablation algorithm
CHENG Shan1, CAI Ziwei1, ZHANG Xujun2, HUANG Yongzhang3, XU Hengshan1
1. College of Electrical Engineering & New Energy, China Three Gorges University,Yichang 443002, China; 2. Electric Power Science Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730070, China; 3. State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, China
Abstract:
To accurately identify the parameters of the low-voltage ride-through (LVRT) model of PV inverters to meet the requirements of safety and reliability of PV grid-connected systems, this paper proposes a multi-stage parameter identification method for that model based on the snow-ablation-optimizer (SAO) algorithm. First, based on the characteristics of output curves of a PV power generation system during LVRT, a mathematical model of PV LVRT control is established, and the transient fault process is analyzed to determine the key control parameters of the LVRT process. Next, a multi-stage identification strategy is proposed for the coupling and correlation between the inner-loop PI control parameters and LVRT. Finally, a PV inverter model is developed based on actual engineering parameters, and the SAO algorithm is used to identify the inner-loop control parameters and LVRT parameters. Simulation case studies demonstrate the effectiveness of the proposed identification method.
Key words:  PV grid-connected inverter  low-voltage ride-through  correlation analysis  snow ablation optimizer (SAO)  parameter identification
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