Fault Diagnosis of Power Transformer Using Particle Swarm Optimization and Extreme Learning Machine Based on DGA

YUAN Haiman; WU Guangning; GAO Bo

doi:10.13296/j.1001-1609.hva.2016.11.029

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Fault Diagnosis of Power Transformer Using Particle Swarm Optimization and Extreme Learning Machine Based on DGA

更新时间：2025-04-26

- Fault Diagnosis of Power Transformer Using Particle Swarm Optimization and Extreme Learning Machine Based on DGA
- High VoltageApparatus Vol. 52, Issue 11, Pages: 176-180+187(2016)
- 作者机构：
  
  西南交通大学电气工程学院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2016.11.029
  CLC： TM41;TP18
- Published：2016
- 稿件说明：
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[1]袁海满,吴广宁,高波.基于DGA的粒子群极限学习机电力变压器故障诊断[J].高压电器,2016,52(11):176-180+187.

YUAN Haiman, WU Guangning, GAO Bo. Fault Diagnosis of Power Transformer Using Particle Swarm Optimization and Extreme Learning Machine Based on DGA[J]. High voltageapparatus, 2016, 52(11): 176-180+187.
[1]袁海满,吴广宁,高波.基于DGA的粒子群极限学习机电力变压器故障诊断[J].高压电器,2016,52(11):176-180+187. DOI： 10.13296/j.1001-1609.hva.2016.11.029.

YUAN Haiman, WU Guangning, GAO Bo. Fault Diagnosis of Power Transformer Using Particle Swarm Optimization and Extreme Learning Machine Based on DGA[J]. High voltageapparatus, 2016, 52(11): 176-180+187. DOI： 10.13296/j.1001-1609.hva.2016.11.029.

摘要

传统单一人工智能方法对变压器故障诊断中采用的大量不完备信息不能够有效处理

导致故障诊断准确率不高。为弥补这一不足

在全面分析粒子群算法（particle swarm optimization

PSO）和极限学习机（extreme learning machine

ELM）各自优势的基础上

构建了一种基于粒子群优化极限学习机的变压器故障诊断方法。该方法以DGA作为特征输入

利用粒子群算法对极限学习机的输入层权值和隐含层阈值进行优化

从而提高变压器故障诊断的精度。实例对比分析表明

与BP神经网络和极限学习机方法相比

粒子群极限学习（PSO-ELM）方法有更高的诊断准确率。

Abstract

Traditional single artificial intelligence method cannot effectively process the huge amount of incomplete fault information from transformer fault diagnosis

resulting in low accuracy of fault diagnosis. In this paper

a novel transformer fault diagnosis method based on particle swarm optimization（PSO） and extreme learning machine（ELM）is proposed by making full use of the advantages of PSO and ELM. In this method

DGA is taken as the feature

PSO algorithm is adopted to optimize ELM’s input layer weights and implicit layer threshold

hence the accuracy of transformer fault diagnosis can be improved. Examples contrast shows that

compared with the BP neural network and the traditional extreme learning machine

the proposed PSO-ELM method has higher diagnosis accuracy.

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