Fault Diagnosis of Converter in Flexible Multi Terminal DC Transmission Based on WSE-RVM

WANG Cuicui; WANG Weiqing; WANG Haiyun; YANG Shaoping

doi:10.13296/j.1001-1609.hva.2018.04.011

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Fault Diagnosis of Converter in Flexible Multi Terminal DC Transmission Based on WSE-RVM

更新时间：2025-04-26

- Fault Diagnosis of Converter in Flexible Multi Terminal DC Transmission Based on WSE-RVM
- High VoltageApparatus Vol. 54, Issue 4, Pages: 72-80(2018)
- 作者机构：
  
  1. 新疆大学电气工程学院
  2. 可再生能源发电与并网技术教育部工程研究中心
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2018.04.011
  CLC： TM46;TM721.1
- Published：2018
- 稿件说明：
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[1]王翠翠,王维庆,王海云,杨少平.基于WSE-RVM的柔性多端直流输电换流器故障诊断[J].高压电器,2018,54(04):72-80.

WANG Cuicui, WANG Weiqing, WANG Haiyun, et al. Fault Diagnosis of Converter in Flexible Multi Terminal DC Transmission Based on WSE-RVM[J]. High voltageapparatus, 2018, 54(4): 72-80.
[1]王翠翠,王维庆,王海云,杨少平.基于WSE-RVM的柔性多端直流输电换流器故障诊断[J].高压电器,2018,54(04):72-80. DOI： 10.13296/j.1001-1609.hva.2018.04.011.

WANG Cuicui, WANG Weiqing, WANG Haiyun, et al. Fault Diagnosis of Converter in Flexible Multi Terminal DC Transmission Based on WSE-RVM[J]. High voltageapparatus, 2018, 54(4): 72-80. DOI： 10.13296/j.1001-1609.hva.2018.04.011.

摘要

为了快速、准确地对柔性多端直流输电系统（VSC-MTDC）中电压源换流器（VSC）的故障进行诊断

提出了一种基于小波奇异熵（WSE）理论和相关向量机（RVM）的诊断方法。首先

在PSCAD/EMTDC中搭建了三端直流输电系统的仿真模型

并对电压源换流器的几种常见故障进行了仿真分析。其次

以VSC-MTDC直流侧电压作为故障信号源

利用小波奇异熵理论对仿真数据进行故障特征提取

将得到的故障特征向量作为相关向量机的输入样本。最后

利用相关向量机对换流器故障进行诊断

结果表明

小波奇异熵理论可以较好地提取电压源换流器的故障特征

相关向量机比同等条件下的支持向量机具有更高的诊断准确率和更短的诊断时间。

Abstract

In order to diagnose the fault of voltage source converter（VSC）in VSC-MTDC more quickly andaccurately

a fault diagnosis method based on wavelet singular entropy（WSE）theory and relevance vector machine（RVM）is proposed.Firstly

based on PSCAD/EMTDC

a simulation model of three-terminal DC transmissionsystem is built

and several common faults of VSC are simulated and analyzed.Secondly

the DC side voltage of VSC-MTDC is used as the fault signal source

using the wavelet singularity entropy theory to extract the fault features ofthe simulation data. The obtained fault feature vector is used as the input sample of RVM.Finally

RVM is used todiagnose the fault of VSC.The final findings show that

the wavelet singular entropy theory can be well used toextract the fault features

and RVM has higher diagnostic accuracy and shorter diagnosis time than SVM under thesame conditions.

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