Wavelet Moment Features Extraction of Surface Discharge Signals on Oil-paper Insulation and Partition of the Discharge Process

DU Jie; CHENG Yingying; ZHOU Feng; XIAO Ji; YANG Huaxiao; WANG Qian; HAO Jian

doi:10.13296/j.1001-1609.hva.2015.01.011

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Wavelet Moment Features Extraction of Surface Discharge Signals on Oil-paper Insulation and Partition of the Discharge Process

更新时间：2025-04-26

- Wavelet Moment Features Extraction of Surface Discharge Signals on Oil-paper Insulation and Partition of the Discharge Process
- High VoltageApparatus Vol. 51, Issue 1, Pages: 61-66(2015)
- 作者机构：
  
  国网重庆市电力公司电力科学研究院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2015.01.011
  CLC： TM403.3
- Published：2015
- 稿件说明：
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[1]杜杰,程瑛颖,周峰,肖冀,杨华潇,王谦,郝建.油纸绝缘沿面放电小波矩特征信息提取及其过程划分[J].高压电器,2015,51(01):61-66.

DU Jie, CHENG Yingying, ZHOU Feng, et al. Wavelet Moment Features Extraction of Surface Discharge Signals on Oil-paper Insulation and Partition of the Discharge Process[J]. High voltageapparatus, 2015, 51(1): 61-66.
[1]杜杰,程瑛颖,周峰,肖冀,杨华潇,王谦,郝建.油纸绝缘沿面放电小波矩特征信息提取及其过程划分[J].高压电器,2015,51(01):61-66. DOI： 10.13296/j.1001-1609.hva.2015.01.011.

DU Jie, CHENG Yingying, ZHOU Feng, et al. Wavelet Moment Features Extraction of Surface Discharge Signals on Oil-paper Insulation and Partition of the Discharge Process[J]. High voltageapparatus, 2015, 51(1): 61-66. DOI： 10.13296/j.1001-1609.hva.2015.01.011.

摘要

油纸绝缘沿面放电是变压器内局部放电的主要形式

研究其产生及发展特性

能有效诊断运行变压器的潜伏性缺陷。笔者基于典型油纸绝缘沿面放电模型

采用恒压法开展局部放电发展特性试验研究。采集沿面放电发展过程中不同时刻的信号样本

提取了不同阶段沿面放电灰度图像的小波矩特征参量。对新特征参量进行无监督聚类分析

聚类结果结合不同时刻的信息将沿面放电发展过程分为初始、发展、稳定、预击穿4个阶段

为沿面放电发展特性研究提出了一种新的方法。

Abstract

Surface discharge on oil-paper insulation can be utilized to effectively diagnose internal latent faults in a transformer.Based on the typical rod-plane electrodes in oil-paper insulation

the authors performed experiments in laboratory to investigate the developing characteristics of partial discharge（PD） by using the constant voltage method.The PD signals were sampled at different time during the development process of surface discharge

and the wavelet moment parameters were extracted from the gray intensity images.By means of the unsupervised clustering method

the surface discharge process were divided into initial stage

expanding stage

stable stage and pre-breakdown stage.This study may provide a new method for analyzing PD developing characteristics.

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