Application of Compound Networks in Fault Diagnosis of Power Transformer

ZHANG Wei-zheng; WANG Xiao-ming; WU Xiao-hui; LI Yan-ming

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Application of Compound Networks in Fault Diagnosis of Power Transformer

更新时间：2025-04-26

- Application of Compound Networks in Fault Diagnosis of Power Transformer
- High VoltageApparatus Issue 5, Pages: 364-367(2007)
- 作者机构：
  
  1. 西安交通大学电气工程学院
  2. 西安交通大学电气工程学院陕西西安710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM407
- Published：2007
- 稿件说明：
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[1]张伟政,汪晓明,吴晓辉,李彦明.组合神经网络在变压器故障诊断中的应用[J].高压电器,2007(05):364-367.

ZHANG Wei-zheng, WANG Xiao-ming, WU Xiao-hui, et al. Application of Compound Networks in Fault Diagnosis of Power Transformer[J]. High voltageapparatus, 2007, (5): 364-367.
[1]张伟政,汪晓明,吴晓辉,李彦明.组合神经网络在变压器故障诊断中的应用[J].高压电器,2007(05):364-367. DOI：

ZHANG Wei-zheng, WANG Xiao-ming, WU Xiao-hui, et al. Application of Compound Networks in Fault Diagnosis of Power Transformer[J]. High voltageapparatus, 2007, (5): 364-367. DOI：

摘要

针对油中溶解气体分析数据的归一化预处理

利用可靠性数据分析特征气体浓度和累积频率的概念

提出了两种新的归一化方法:特征浓度归一化法和混合归一化法

引入Fisher准则函数来评价两种预处理方法的效果。检验结果表明

这两种归一化的数据预处理方法可获得类间均值差值较大、类内离散度小的效果。运用不同的归一化预处理方法对故障变压器的色谱数据进行处理后作为训练样本

对CP算法的组合神经网络进行训练。检验样本的诊断结果表明

新的归一化预处理方法能够提高网络诊断的准确率。

Abstract

Using the concepts of typical gas’s concentration and cumulative frequency in analysis of the reliability data for dealing with the pretreatment of data of dissolved gas analysis（DGA）

two new normalized methods which named characteristic normalization and mix normalization were presented in this paper.The Fisher rule to evaluate the results of the two pretreatment methods was also introduced.The evaluation of the results indicated that both of the two data pretreatment methods could achieve the purpose of big difference in the value of mean between classes and small difference in dispersion of a class.The DGA data of the failure transformers were treated by different normalization methods as the training samples

and then the samples were trained in the compound neural networks which use the CP algorithm.The diagnosis results of the test samples indicated that the new methods may help to improve the precision of network diagnosis.

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Related Author

WANG Xiao-ming1

HE Ping1

WU Hua1

CHEN Zhen-gang1

OU YANG Jin2

LI Yan-ming3

YE Yulin

LIU Sen

Related Institution

Ultra High Voltage Transmission Subcompany of Jiangxi Electric Power Corporation

Jiangxi Nanchang Power Corporation

School of Electrical Engineering, Xi’an Jiaotong University

China Nuclear Power Engineering Co., Ltd.

School of Electrical Engineering, Hebei University of Science and Technology

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