Application of CP Compound Modular Neural Network in DGA Based Power Transformer Fault Diagnosis

WANG Xiao-ming1; HE Ping1; WU Hua1; CHEN Zhen-gang1; OU YANG Jin2; LI Yan-ming3

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Application of CP Compound Modular Neural Network in DGA Based Power Transformer Fault Diagnosis

更新时间：2025-04-26

- Application of CP Compound Modular Neural Network in DGA Based Power Transformer Fault Diagnosis
- High VoltageApparatus Vol. 44, Issue 6, Pages: 543-547(2008)
- 作者机构：
  
  1. 江西省电力公司超高压分公司
  2. 江西省南昌市供电公司
  3. 西安交通大学电气工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM41;TP183
- Published：2008
- 稿件说明：
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[1]汪晓明,何萍,吴花,陈振刚,欧阳瑾,李彦明.CP组合神经网络在基于DGA的变压器绝缘故障诊断中的应用[J].高压电器,2008,44(06):543-547.

WANG Xiao-ming1, HE Ping1, WU Hua1, et al. Application of CP Compound Modular Neural Network in DGA Based Power Transformer Fault Diagnosis[J]. High voltageapparatus, 2008, 44(6): 543-547.
[1]汪晓明,何萍,吴花,陈振刚,欧阳瑾,李彦明.CP组合神经网络在基于DGA的变压器绝缘故障诊断中的应用[J].高压电器,2008,44(06):543-547. DOI：

WANG Xiao-ming1, HE Ping1, WU Hua1, et al. Application of CP Compound Modular Neural Network in DGA Based Power Transformer Fault Diagnosis[J]. High voltageapparatus, 2008, 44(6): 543-547. DOI：

摘要

对向传播（CP）算法是一种有教师学习和无教师学习算法的混合体

既具有良好的模式识别性能

又能很好地解决反馈型神经网络的收敛问题。笔者提出了基于CP分类器人工神经网络的变压器故障诊断方法

建立了CP组合神经网络模型

通过比较不同训练情况下的正判率来确定CP网络中的训练次数和竞争层神经元的个数。实例证明该模型诊断结果的正判率比改良电协研法和IEC三比值法有较大的提高

具有较高的诊断准确率和应用价值。

Abstract

Counter propagation（CP） arithmetic is one kind mixture arithmetic complsed supervised learning algorithm and unsupervised learning algorithm.It has good pattern recognition performance

and can solve the convergence problem of feedback neural network.This paper presents a fault diagnosis method for power transformer based on CP neural network

and builds a compound neural networks model.The training times and the number of neurons on a competition level in CP network are determined by comparing the results in different training situations.The diagnostic examples indicate the diagnostic correctness of the proposed method is higher than that of the improved three rations method.

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Related Author

WU Xiao-hui1

WANG Song1

FANG Xiao-ming1

LI Yan-mu2

LI Yan-ming1

ZHANG Wei-zheng

WANG Xiao-ming

WU Xiao-hui

Related Institution

School of Mechanical and Electrical,Xi’an University of Architecture and Technology

School of Electrical Engineering,Xi’an Jiaotong University

西安交通大学电气工程学院陕西西安710049

China Nuclear Power Engineering Co., Ltd.

School of Electrical Engineering, Hebei University of Science and Technology

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