基于模糊C均值聚类的高铁动车组电缆终端局部放电识别

杨燕花; 陈珍宝; 曹晗; 张彦林; 刘凯; 陈奎; 高国强

doi:10.13890/j.issn.1000-128X.2024.01.240

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基于模糊C均值聚类的高铁动车组电缆终端局部放电识别

智能化技术 | 更新时间：2024-08-01

- 基于模糊C均值聚类的高铁动车组电缆终端局部放电识别
- Identification of partial discharges in cable terminals of high-speed EMUs based on fuzzy C-means clustering
- 机车电传动 2024年第3期页码：156-163
- 作者机构：
  
  1.中车株洲电力机车有限公司，湖南株洲 412001
  2.西南交通大学电气工程学院，四川成都 611756
- 作者简介：
  
  刘　凯，男，博士，副教授，主要研究方向为电力设备多物理场计算与状态分析、电气设备在线监测及故障诊断检测； E-mail: liukai@swjtu.edu.cn
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2024.01.240
  中图分类号： U292.91⁺.4
- 纸质出版日期：2024-05-10，
  
  收稿日期：2023-11-06，
  
  修回日期：2023-12-22，
- 稿件说明：
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杨燕花, 陈珍宝, 曹晗, 等. 基于模糊C均值聚类的高铁动车组电缆终端局部放电识别[J]. 机车电传动, 2024(3): 156-163.

YANG Yanhua, CEHN Zhenbao, CAO Han, et al. Identification of partial discharges in cable terminals of high-speed EMUs based on fuzzy C-means clustering[J]. Electric drive for locomotives,2024(3): 156-163.
杨燕花, 陈珍宝, 曹晗, 等. 基于模糊C均值聚类的高铁动车组电缆终端局部放电识别[J]. 机车电传动, 2024(3): 156-163. DOI：10.13890/j.issn.1000-128X.2024.01.240.

YANG Yanhua, CEHN Zhenbao, CAO Han, et al. Identification of partial discharges in cable terminals of high-speed EMUs based on fuzzy C-means clustering[J]. Electric drive for locomotives,2024(3): 156-163. DOI：10.13890/j.issn.1000-128X.2024.01.240.

摘要

局部放电检测作为一种诊断车载电缆终端绝缘状态的有效手段，在列车实际运行环境中面临强干扰问题，为此文章提出了一种基于波形参数分析和模糊C均值聚类的车载电缆终端局放脉冲干扰分离策略。在实验室搭建了局部放电测试平台并采用高频电流法（HFCT）获取了电缆终端的局放信号和典型脉冲干扰信号，通过对脉冲单波进行包络处理，提取脉冲的3个参数作为特征向量，然后采用模糊C均值聚类方法对局放信号与脉冲干扰信号进行分离。试验结果表明，该方法能够有效地将局放信号与脉冲干扰信号分离，减小脉冲干扰信号对局部放电检测的影响，对提高局放手段评估车载电缆终端绝缘状态的准确性具有一定意义。

Abstract

As an effective means to diagnose the insulation status of on-board cable terminals

partial discharge detection faces strong interference in the actual operating environment of trains. To address this issue

this paper proposed a strategy for separating partial discharge pulses of on-board cable terminals based on waveform parameter analysis and fuzzy C-means clustering. A partial discharge test platform was built in the laboratory

and high-frequency current transducers (HFCT) were used to acquire partial discharge signals and the typical pulse interference signals from cable terminals. By performing envelope analysis on individual pulses

three parameters of the pulses were extracted as the feature vectors. Subsequently

fuzzy C-means clustering was employed to separate the partial discharge signals from the pulse interference signals. The experimental results demonstrate that the proposed method can effectively separate partial discharge signals from pulse interference signals

reducing the impact of pulse interference on partial discharge detection

and is of some significance in improving the accuracy of assessing the insulation status of the on-board cable terminals through partial discharge means.

关键词

动车组电缆终端局部放电脉冲干扰模糊C均值聚类

Keywords

EMUcable terminalpartial dischargepulse interferencefuzzy C-means clustering

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