Application of Adaptive Hierarchical Multi-class SVM to Transformer Fault Diagnosis

YANG Hong1; GU Shi-fu1; TAO Jia-gui1; GOU Jian2

doi:10.13296/j.1001-1609.hva.2010.05.021

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Application of Adaptive Hierarchical Multi-class SVM to Transformer Fault Diagnosis

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- Application of Adaptive Hierarchical Multi-class SVM to Transformer Fault Diagnosis
- High VoltageApparatus Vol. 46, Issue 5, Pages: 49-52(2010)
- 作者机构：
  
  1. 西华大学电气信息学院
  2. 四川绵阳电业局
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2010.05.021
  CLC： TM407
- Published：2010
- 稿件说明：
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[1]杨洪,古世甫,陶加贵,苟建.自适应分级多分类支持向量机在变压器故障诊断中的应用[J].高压电器,2010,46(05):49-52.

YANG Hong1, GU Shi-fu1, TAO Jia-gui1, et al. Application of Adaptive Hierarchical Multi-class SVM to Transformer Fault Diagnosis[J]. High voltageapparatus, 2010, 46(5): 49-52.
[1]杨洪,古世甫,陶加贵,苟建.自适应分级多分类支持向量机在变压器故障诊断中的应用[J].高压电器,2010,46(05):49-52. DOI： 10.13296/j.1001-1609.hva.2010.05.021.

YANG Hong1, GU Shi-fu1, TAO Jia-gui1, et al. Application of Adaptive Hierarchical Multi-class SVM to Transformer Fault Diagnosis[J]. High voltageapparatus, 2010, 46(5): 49-52. DOI： 10.13296/j.1001-1609.hva.2010.05.021.

摘要

以变压器油中溶解气体和变压器故障之间的关系为基础

提出了一种自适应分级多分类支持向量机变压器故障诊断方法。此方法基于模式识别特征提取的思想

采用不同的输入向量

对变压器有无故障和故障类型判别时

采取分级决策结构。采用自适应优化算法对多分类支持向量机进行优化

通过诊断效果和不同类型故障识别率的比较

得出变压器油中溶解气体的组分含量比值更能反映变压器故障类型

最终测试效果比较和支持向量机参数分析

可以看出该方法具有较高的准确率和良好的泛化能力。

Abstract

On the basis of the relationship between dissolved gases in transformer oil and transformer fault

a tran sformer fault diagnosis method adopting adaptive hierarchical multi-class SVM is proposed.Based on the concept of feature extraction in pattern recognition

a hierarchical structure is employed with different input vectors for transformer fault judgment and fault type identification.By optimizing the multi-class SVM with adaptive optimization method and comparing the diagnosis effects and recognition rates of different fault types

it indicates that the composition ratio of dissolved gases in transformer oil is more sensitive to transformer fault types.Comparison of test results and analysis of support vector machine parameters demonstrate that the proposed method has high accuracy and excellent generalization.

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