基于并联卷积神经网络的多端直流输电线路故障诊断

王浩; 杨东升; 周博文; 高筱婷; 庞永恒

doi:10.7500/AEPS20191124003

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基于并联卷积神经网络的多端直流输电线路故障诊断

学术研究 | 更新时间：2025-06-16

- 基于并联卷积神经网络的多端直流输电线路故障诊断
- Fault Diagnosis of Multi-terminal HVDC Transmission Line Based on Parallel Convolutional Neural Network
- 电力系统自动化-方正平台 2020年44卷第12期页码：84-92
- 作者机构：
  
  东北大学信息科学与工程学院，辽宁省沈阳市 110819
- 作者简介：
  
  王浩(1992—)，男，硕士研究生，主要研究方向：电力系统故障诊断。E-mail：hhao2233@gmail.com
  杨东升(1977—)，男，通信作者，博士，教授，博士生导师，主要研究方向：电力系统自动化。E-mail:yangdongsheng@mail.neu.edu.cn
  周博文（1987—），男，博士，讲师，主要研究方向：电动汽车及其并网控制。E-mail:zhoubowen@ise.neu.edu.cn
- 基金信息：
  
  国家自然科学基金-辽宁联合基金资助项目(U1908217)
- DOI：10.7500/AEPS20191124003
  中图分类号：
- 收稿日期：2019-11-24，
  
  修回日期：2019-12-04，
  
  网络出版日期：2020-03-18，
  
  纸质出版日期：2020-06-25
- 稿件说明：
移动端阅览
王浩,杨东升,周博文等.基于并联卷积神经网络的多端直流输电线路故障诊断[J].电力系统自动化,2020,44(12):84-92.

WANG Hao,YANG Dongsheng,ZHOU Bowen,et al.Fault Diagnosis of Multi-terminal HVDC Transmission Line Based on Parallel Convolutional Neural Network[J].Automation of Electric Power Systems,2020,44(12):84-92.
王浩,杨东升,周博文等.基于并联卷积神经网络的多端直流输电线路故障诊断[J].电力系统自动化,2020,44(12):84-92. DOI： 10.7500/AEPS20191124003.

WANG Hao,YANG Dongsheng,ZHOU Bowen,et al.Fault Diagnosis of Multi-terminal HVDC Transmission Line Based on Parallel Convolutional Neural Network[J].Automation of Electric Power Systems,2020,44(12):84-92. DOI： 10.7500/AEPS20191124003.

摘要

针对多端直流输电(MTDC)线路故障时存在故障电流上升速度快、峰值大、不易定位等特点，提出一种兼顾快速性与准确性的MTDC线路故障诊断方法。首先，分析MTDC线路故障信号波形的幅值特征和频率特征，研究基于信号波形幅值变化的故障幅值特征提取方法和基于小波包分析的故障频率特征提取方法，进而形成基于幅值-频率特征的MTDC线路故障诊断方法。其次，构建具有故障分类支路和故障定位支路的双支路结构卷积神经网络——并联卷积神经网络（P-CNN），提出基于迁移学习的P-CNN训练方法。最后，仿真验证基于P-CNN的MTDC线路故障诊断方法满足故障诊断的快速性要求，且其并联结构相比于其他人工智能故障诊断方法更具有准确性和可拓展性。

Abstract

In view of the fault characteristics of multi-terminal high voltage direct current (MTDC) transmission lines

such as rapid rising speed， high fault peak current and difficulty in fault location

a fault diagnosis method with both rapidity and accuracy is proposed for MTDC system. Firstly

the amplitude and frequency characteristics of fault signal waveforms of MTDC transmission line faults are analyzed. The fault amplitude and frequency characteristic extraction methods are studied

based on amplitude variation of signal waveforms and wavelet packet analysis

respectively. Then

the fault diagnosis method of MTDC transmission system based on amplitude-frequency characteristics is obtained. Secondly

the parallel convolutional neural network (P-CNN) with fault classification and fault location dual branch structure is constructed

and the training method of P-CNN based on transfer learning is proposed. Finally

the simulation verifies that the fault diagnosis method of MTDC system based on P-CNN can meet the speed requirement

and the parallel structure is more accurate and expandable than other artificial intelligence fault diagnosis methods.

关键词

Keywords

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