Recognition of Partial Discharge Distribution Patterns Based on Morlet Wavelet Transform and Neural Network

WANG Ping; FU li-hua; HU Guang-zhen

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Recognition of Partial Discharge Distribution Patterns Based on Morlet Wavelet Transform and Neural Network

更新时间：2025-05-17

- Recognition of Partial Discharge Distribution Patterns Based on Morlet Wavelet Transform and Neural Network
- High VoltageApparatus Issue 5, (2005)
- 作者机构：
  
  1. 鞍山科技大学
  2. 鞍山科技大学辽宁鞍山114044
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM835
- Published：2005
- 稿件说明：
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[1]王萍,傅立华,胡广振.一种基于Morlet小波分析与神经网络技术的局部放电识别方法[J].高压电器,2005(05):49-52.

WANG Ping, FU li-hua, HU Guang-zhen. Recognition of Partial Discharge Distribution Patterns Based on Morlet Wavelet Transform and Neural Network[J]. High voltageapparatus, 2005, (5).
[1]王萍,傅立华,胡广振.一种基于Morlet小波分析与神经网络技术的局部放电识别方法[J].高压电器,2005(05):49-52. DOI：

WANG Ping, FU li-hua, HU Guang-zhen. Recognition of Partial Discharge Distribution Patterns Based on Morlet Wavelet Transform and Neural Network[J]. High voltageapparatus, 2005, (5). DOI：

摘要

小波变换技术适合电机局部放电的模式识别

笔者从一个新的角度来描述了局部放电信号的特性。研究表明

用Morlet小波变换三维分布图计算出的特征参数可以区分空气中的尖尖放电、尖板放电和沿面放电。理论和仿真实验分析表明

小波变换三维分布图提取的特征参数可以作为识别局部放电的新特征量

这些特征量作为人工神经网络的输入量可单独使用也可结合其它方法使用。

Abstract

This paper describes the partial discharge（PD） signal characteristics using wavelet transform techniques.Research shows that the feature parameters by the Morlet wavelet transform 3-D distribution figures can be used to distinguish point to point PD

point to plate PD and surface PD.Theoretical analysis and simulating tests show that such feature parameters can be used as a new practical features of PD signal recognition

which can be used solely or combined with other methods as the inputs of artificial meural network.

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