Application of Signals Separated Band Energy Spectrum and Mahalanobis Clustering Algorithm for Switchgear Partial Discharge Pattern Recognition

CHEN Pan; YAO Chenguo; LIAO Ruijin; CHEN Yu; MI Yan

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Application of Signals Separated Band Energy Spectrum and Mahalanobis Clustering Algorithm for Switchgear Partial Discharge Pattern Recognition

更新时间：2025-12-08

- Application of Signals Separated Band Energy Spectrum and Mahalanobis Clustering Algorithm for Switchgear Partial Discharge Pattern Recognition
- Vol. 41, Issue 10, Pages: 3332-3341(2015)
- 作者机构：
  
  1. 重庆大学输配电装备及系统安全与新技术国家重点实验室
  2. 2. 国网重庆市电力公司江北供电分公司
  3. 南方电网中山市供电公司
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：30 October 2015，
  
  Published：2015
- 稿件说明：
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CHEN Pan, YAO Chenguo, LIAO Ruijin, et al. Application of Signals Separated Band Energy Spectrum and Mahalanobis Clustering Algorithm for Switchgear Partial Discharge Pattern Recognition[J]. 2015, 41(10): 3332-3341.
DOI：

CHEN Pan, YAO Chenguo, LIAO Ruijin, et al. Application of Signals Separated Band Energy Spectrum and Mahalanobis Clustering Algorithm for Switchgear Partial Discharge Pattern Recognition[J]. 2015, 41(10): 3332-3341. DOI：

摘要

针对传统超高频（UHF）检测法对系统采样频率、数据处理存储要求严苛及成本较高的缺点

融合混频降频、功率检波技术

研制出一套较经济的高压开关柜超高频在线监测系统。采用该系统对沿面放电、气隙放电、针–板放电和自由微粒放电共4种典型缺陷模型的局部放电信号进行监测

获得信号在不同频段的能量谱;并在此基础上提出了将局部放电的分频段能量作为特征向量、利用马氏距离算法进行聚类分析的高压开关柜局部放电模式识别方法。研究结果表明:不同放电类型在频域内的能量分布差异较大

相同的放电类型在频域内的能量分布差异较小;不同放电类型在能量比重三角图中表现出显著的区域聚集特性。该局部放电模式识别方法对4种典型缺陷放电类型的识别准确率达到99.125%。

Abstract

In order to overcome defects of traditional ultra high frequency（UHF） detection such as high cost

strict requirements on system sampling rate and data storage

we developed an economical UHF on-line monitoring system for HV switchgear by incorporating down-mixing with power detection technology. We used the proposed system to monitor the partial discharge（PD） signals of four typical defect models

namely surface discharge

air gap discharge

needle-plate discharge and free particle discharge

and obtained their energy spectrum in different frequency bands. Then

taking the sub-band energy of PD signals as eigenvectors

we proposed a PD pattern recognition method for HV switchgear by using Mahalanobis distance as clustering analysis tool.The results show that

the energy distributions of different PD models are more significantly different than the same PD models

and that different types of PD apparently present a feature of regional clustering in energy triangle plot. Moreover

by using the proposed PD recognition method

the accurate rate of PD recognition for the four typical PD defects is 99.125%.

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