An Improved Wavelet Network with Application to the Fault Diagnosis of Oil-immersed Transformer

FANG Jian1; PENG Hui1; LI Zi-pin1; SHU Nai-qiu1

doi:10.13296/j.1001-1609.hva.2011.11.016

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An Improved Wavelet Network with Application to the Fault Diagnosis of Oil-immersed Transformer

更新时间：2025-04-26

- An Improved Wavelet Network with Application to the Fault Diagnosis of Oil-immersed Transformer
- High VoltageApparatus Vol. 47, Issue 11, Pages: 115-120(2011)
- 作者机构：
  
  1. 武汉大学电气工程学院
  2. 广州供电局试验研究所
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2011.11.016
  CLC： TM411
- Published：2011
- 稿件说明：
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[1]方健,彭辉,李自品,舒乃秋.改进小波网络在油浸式变压器故障诊断中的应用[J].高压电器,2011,47(11):115-120.

FANG Jian1, PENG Hui1, LI Zi-pin1, et al. An Improved Wavelet Network with Application to the Fault Diagnosis of Oil-immersed Transformer[J]. High voltageapparatus, 2011, 47(11): 115-120.
[1]方健,彭辉,李自品,舒乃秋.改进小波网络在油浸式变压器故障诊断中的应用[J].高压电器,2011,47(11):115-120. DOI： 10.13296/j.1001-1609.hva.2011.11.016.

FANG Jian1, PENG Hui1, LI Zi-pin1, et al. An Improved Wavelet Network with Application to the Fault Diagnosis of Oil-immersed Transformer[J]. High voltageapparatus, 2011, 47(11): 115-120. DOI： 10.13296/j.1001-1609.hva.2011.11.016.

摘要

小波网络是近年来神经网络研究中的一个新分支

是结合小波变换理论与神经网络的思想构造的一种新的神经网络模型。笔者构造的前馈小波神经网络是将小波分析与BP神经网络融合

以Gauss小波及其伸缩平移系作为隐含层小波基函数并且对小波神经网络做了一些改进。选择了300组油中溶解气体含量作为前馈小波神经网络训练及故障识别的样本

并对仿真结果进行分析。实验结果显示:文中构造的小波神经网络适合变压器故障诊断

其性能优于改良IEC法

传统BP神经网络法和基于主成分分析法的BP神经网络。该方法已经运用在实际的变压器故障诊断项目中。

Abstract

This paper constructs a feed-forward wavelet network by combining the wavelet analysis with BP neural network.The wavelet network takes Gauss wavelet and its telescopic translation system as the hidden layer of the wavelet basic function

and the wavelet neural network is improved.300 measured content data of dissolved gas in oil are selected as the samples for training the feed-forward wavelet network and faults recognition

and the simulation results are compared and analyzed.Experimental results demonstrate that the proposed improved wavelet network adapts to transformer fault diagnosis with better performance than the IEC

BPNN and the BP neural network based on principal component analysis.This method has been used in the actual transformer fault diagnosis project.

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