Transformer Fault Diagnosis Algorithm Based on PSO-ELM Fusion Dynamically Weighted Ada Boost

HUANG Xinbo; WANG Xiang; TIAN Yi; LI Licheng; CAO Wen

doi:10.13296/j.1001-1609.hva.2020.05.007

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Transformer Fault Diagnosis Algorithm Based on PSO-ELM Fusion Dynamically Weighted Ada Boost

更新时间：2025-04-26

- Transformer Fault Diagnosis Algorithm Based on PSO-ELM Fusion Dynamically Weighted Ada Boost
- High VoltageApparatus Vol. 56, Issue 5, Pages: 39-46(2020)
- 作者机构：
  
  1. 西安工程大学电子信息学院
  2. 华南理工大学电力学院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2020.05.007
  CLC： TP18;TM407
- Published：2020
- 稿件说明：
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[1]黄新波,王享,田毅,李立浧,曹雯.基于PSO-ELM融合动态加权AdaBoost的变压器故障诊断方法[J].高压电器,2020,56(05):39-46.

HUANG Xinbo, WANG Xiang, TIAN Yi, et al. Transformer Fault Diagnosis Algorithm Based on PSO-ELM Fusion Dynamically Weighted Ada Boost[J]. High voltageapparatus, 2020, 56(5): 39-46.
[1]黄新波,王享,田毅,李立浧,曹雯.基于PSO-ELM融合动态加权AdaBoost的变压器故障诊断方法[J].高压电器,2020,56(05):39-46. DOI： 10.13296/j.1001-1609.hva.2020.05.007.

HUANG Xinbo, WANG Xiang, TIAN Yi, et al. Transformer Fault Diagnosis Algorithm Based on PSO-ELM Fusion Dynamically Weighted Ada Boost[J]. High voltageapparatus, 2020, 56(5): 39-46. DOI： 10.13296/j.1001-1609.hva.2020.05.007.

摘要

油中溶解气体分析（DGA）是电力变压器故障诊断的一种常用方法。然而

基于DGA的传统方法不能建立精确的数学模型

单一智能诊断方法在实际应用中存在分类能力不足等问题

导致诊断结果准确率较低。文中提出一种基于PSO-ELM融合动态加权AdaBoost的变压器故障诊断方法。首先利用PSO算法对ELM模型进行优化

得到PSO-ELM模型

然后利用多分类AdaBoost算法对PSO-ELM模型进行重复迭代

得到带有不同权重的弱分类器

通过计算各分类器对测试样本的分错率

不断调整每个弱分类器的权重系数

最终利用所有弱分类器加权投票对监测数据进行故障诊断。实验结果表明

相比于BP神经网络、ELM、支持向量机（SVM）

文中算法的诊断准确率分别提高16.02%、9.78%、5.62%。

Abstract

For fault diagnosis of power transformers

the traditional methods based on the dissolved gas analysis（DGA） DGA cannot establish accurate mathematical models

and single intelligent diagnosis method is unsatisfactory in classification ability in practical applications

resulting in lower accuracy of diagnosis results.In this paper

a transformer fault diagnosis method

which is based on PSO-ELM fusion dynamically weighted AdaBoost

is proposed.Firstly

the PSO algorithm is used to optimize the ELM model to obtain the PSO-ELM model.Then

the PSO-ELM model is iterated through the multi-class AdaBoost algorithm to obtain weak classifiers with different weights.By calculating the classification error rate of test sample for each classifier

the weight coefficient of each weak classifier is continuously adjusted

the weighted vote of all weak classifiers is eventually used for fault diagnosis based on the monitoring data.Experimental results show that

compared with BP neural network

ELM

and support vector machine（SVM）

the proposed algorithm raises the diagnosis accuracy by 16.02%

9.78%

and 5.62%

respectively.

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