Power Transformer Fault Diagnosis via Neural Network Based on Particle Swarm Optimization with Neighborhood Operator

JIA Rong; XU Qi-hui; LI Hui; LIU Wei

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Power Transformer Fault Diagnosis via Neural Network Based on Particle Swarm Optimization with Neighborhood Operator

更新时间：2025-04-26

- Power Transformer Fault Diagnosis via Neural Network Based on Particle Swarm Optimization with Neighborhood Operator
- High VoltageApparatus Issue 1, Pages: 8-10+19(2008)
- 作者机构：
  
  1. 西安理工大学电力工程系
  2. 西安理工大学电力工程系陕西西安710048
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM407;TP183
- Published：2008
- 稿件说明：
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[1]贾嵘,徐其惠,李辉,刘伟.基于邻域粒子群优化神经网络的变压器故障诊断[J].高压电器,2008(01):8-10+19.

JIA Rong, XU Qi-hui, LI Hui, et al. Power Transformer Fault Diagnosis via Neural Network Based on Particle Swarm Optimization with Neighborhood Operator[J]. High voltageapparatus, 2008, (1): 8-10+19.
[1]贾嵘,徐其惠,李辉,刘伟.基于邻域粒子群优化神经网络的变压器故障诊断[J].高压电器,2008(01):8-10+19. DOI：

JIA Rong, XU Qi-hui, LI Hui, et al. Power Transformer Fault Diagnosis via Neural Network Based on Particle Swarm Optimization with Neighborhood Operator[J]. High voltageapparatus, 2008, (1): 8-10+19. DOI：

摘要

为了提高变压器故障诊断正判率

提出了一种邻域粒子群算法优化BP神经网络的电力变压器油中气体分析（DGA）方法

即通过相关统计分析和数据的预处理

选择变压器油中典型气体作为神经网络的输入

然后利用训练好的邻域粒子群算法优化后的神经网络进行变压器故障类型诊断。试验结果表明

该类方法具有很好的分类效果

较好地解决了变压器放电和过热共存时故障的难分辨问题

对故障类型的正判率较高。

Abstract

In order to improve the correct judgement rate in power transformer fault diagnosis

this paper investigates a dissolved gas analysis method of transformer via neural network based on particle swarm optimization with neighborhood operator.Based on correlation analysis and data pretreatment

some typical gases in transformer oil are selected as the input of neural network for training

then the fault diagnosis is accomplished via the trained and optimized neural network.The experimental results show that this method gains good classification result

and can identify faults under the difficult situation where transformer overheat and partial discharge coexist.Moreover

this method works with a higher correct judgement rate.

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Related Author

JIA Rong1

LI Hong-bin2

KANG Hui-xi2

HONG Gang1

CHENG Jiatang

DUAN Zhimei

XIONG Yan

AI Li

Related Institution

Department of Electrical Engineering,Xi′an University of Technology,Xi′an

Gansu Tou River Hydropower Development Ltd.

The Engineering College of Honghe University

China Nuclear Power Engineering Co., Ltd.

School of Electrical Engineering, Hebei University of Science and Technology

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