Fault Diagnosis of Transformer Based on LS-TSVM Optimized by Bat Algorithm

CHEN Huan; PENG Hui; SHU Naiqiu; ZHANG Kaixuan; WEI An

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Fault Diagnosis of Transformer Based on LS-TSVM Optimized by Bat Algorithm

更新时间：2025-12-08

- Fault Diagnosis of Transformer Based on LS-TSVM Optimized by Bat Algorithm
- Vol. 44, Issue 11, Pages: 3664-3671(2018)
- 作者机构：
  
  武汉大学电气工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：22 November 2018，
  
  Published：2018
- 稿件说明：
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CHEN Huan, PENG Hui, SHU Naiqiu, et al. Fault Diagnosis of Transformer Based on LS-TSVM Optimized by Bat Algorithm[J]. 2018, 44(11): 3664-3671.
DOI：

CHEN Huan, PENG Hui, SHU Naiqiu, et al. Fault Diagnosis of Transformer Based on LS-TSVM Optimized by Bat Algorithm[J]. 2018, 44(11): 3664-3671. DOI：

摘要

为了提高变压器的故障诊断精度

提出了一种基于蝙蝠算法（BA）优化最小二乘双支持向量机（LS-TSVM）的变压器故障诊断方法。该方法针对变压器故障诊断过程中的多分类问题

通过计算类间相异度矩阵自下而上构建哈夫曼树

结合LS-TSVM建立了多类分类故障诊断模型

然后采用蝙蝠算法对模型中LS-TSVM分类器的参数进行优化。利用该方法对变压器进行故障诊断

实例仿真结果表明:与粒子群优化支持向量机（PSO-SVM）方法相比

所提方法不仅训练时间显著缩短

而且故障诊断精度更高

对于高温过热、低能放电故障的诊断精度均明显高于PSO-SVM方法。仿真结果说明所提方法在变压器故障诊断中具有较高的优越性。

Abstract

In order to improve the fault diagnosis accuracy of transformer

we propose a method which is based on least squares twin support vector machine（LS-TSVM） optimized by bat algorithm（BA）. To solve the multi-classification problem of transformer fault diagnosis in this method

a Huffman tree is established from the bottom to up by calculating the inter class dissimilarity matrix

and a multi-classification fault diagnosis model is built based on the LS-TSVM

then a bat algorithm is used to optimize the parameters of the LS-TSVM classifier in the model. The simulation results of using this method to diagnose the fault of a transformer show that

compared with particle swarm optimization-support vector machine（PSO-SVM）

the proposed method not only shortens the training time

but also achieves a higher accuracy. The diagnostic accuracy for high-temperature overheating and low-energy discharge faults is obviously higher than that of PSO-SVM. The simulation results show that the method has high superiority in transformer fault diagnosis.

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