上海交通大学学报

上海交通大学学报 >

2025 , Vol. 59 >Issue 12: 1805 - 1814

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.653

新型电力系统与综合能源

基于神经网络的LCL型单相并网逆变器稳定性分析

  • 韩华玲 ,
  • 贾一超 ,
  • 马子涵 ,
  • 邓俊 ,
  • 黄萌
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  • 1 中国电力科学院研究院有限公司 南京分院, 南京 210009
    2 武汉大学 电气与自动化学院, 武汉 430072
    3 综合能源电力装备及系统安全湖北省重点实验室, 武汉 430072
    4 国网陕西省电力有限公司 电力科学研究院, 西安 710100
韩华玲(1985—),助理工程师,从事电力系统接入及方法的研究.
马子涵,硕士生;E-mail:zihanma1108@whu.edu.cn.

收稿日期: 2023-12-29

  修回日期: 2024-03-10

  录用日期: 2024-03-20

  网络出版日期: 2024-03-28

基金资助

国家电网有限公司科技项目(4000-202355078A-1-1-ZN)

收起

Stability Analysis of LCL Grid-Connected Inverter Based on Neural Network

  • HAN Hualing ,
  • JIA Yichao ,
  • MA Zihan ,
  • DENG Jun ,
  • HUANG Meng
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  • 1 Nanjing Branch, China Electric Power Research Institute Co., Ltd., Nanjing 210009, China
    2 School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
    3 Hubei Key Laboratory of Power Equipment and System Security for Integrated Energy, Wuhan 430072, China
    4 Electric Power Research Institute, State Grid Shaanxi Electric Power Co., Ltd., Xi’an 710100, China

Received date: 2023-12-29

  Revised date: 2024-03-10

  Accepted date: 2024-03-20

  Online published: 2024-03-28

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摘要

LCL型并网逆变器的参数不确定性严重影响新能源发电的电能质量,因此需要对参数摄动情况下逆变器的稳定情况进行分析.针对上述问题,首先建立LCL型单相并网逆变器状态空间模型,提出基于参数模型的神经网络建模方法.通过参数特征分析,得到基于参数分布的训练数据集,在神经网络中对数据集进行训练,输出稳定性判别结果.最后,通过MATLAB/Simulink仿真与实验平台验证所提基于神经网络分析逆变器稳定性方法的有效性.

关键词： LCL型单相并网逆变器; 稳定性分析; 神经网络; 反向传播; 二分类

本文引用格式

韩华玲 , 贾一超 , 马子涵 , 邓俊 , 黄萌 . 基于神经网络的LCL型单相并网逆变器稳定性分析[J]. 上海交通大学学报, 2025 , 59(12) : 1805 -1814 . DOI: 10.16183/j.cnki.jsjtu.2023.653

Abstract

The parameter uncertainty of LCL type grid-connected inverter can significantly affect the power quality of renewable energy, making it essential to analyze the stability of the inverter under the parameter inception. To solve these problems, this paper establishes a state space model of LCL type single-phase grid-connected inverter and proposes a neural network modeling method based on the parameter model. Through the parameter characterization, a training dataset based on the parameter distribution is obtained. This dataset is then trained in the neural network to produce the stability discrimination results. Finally, the effectiveness of the proposed method is verified by MATLAB/Simulink simulation and the experimental platform.

Key words： LCL type single-phase grid-connected inverter; stability analysis; neural network; back propagation; binary classification

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