Diagnosis Methods for Transformer Faults Based on Vibration Signal Under Fluctuating Operating Conditions

ZHAO Lihua; ZHANG Zhendong; ZHANG Jiangong; HUANG Xiaolong; REN Junwen

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Diagnosis Methods for Transformer Faults Based on Vibration Signal Under Fluctuating Operating Conditions

更新时间：2025-12-08

- Diagnosis Methods for Transformer Faults Based on Vibration Signal Under Fluctuating Operating Conditions
- Vol. 46, Issue 11, Pages: 3925-3933(2020)
- 作者机构：
  
  1. 四川大学电气工程学院
  2. 中国电力科学研究院有限公司电网环境保护国家重点实验室
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：30 November 2020，
  
  Published：2020
- 稿件说明：
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ZHAO Lihua, ZHANG Zhendong, ZHANG Jiangong, et al. Diagnosis Methods for Transformer Faults Based on Vibration Signal Under Fluctuating Operating Conditions[J]. 2020, 46(11): 3925-3933.
DOI：

ZHAO Lihua, ZHANG Zhendong, ZHANG Jiangong, et al. Diagnosis Methods for Transformer Faults Based on Vibration Signal Under Fluctuating Operating Conditions[J]. 2020, 46(11): 3925-3933. DOI：

摘要

为了在运行工况波动下利用变压器振动信号进行故障诊断

采集了不同负载电流下绕组松动与正常和不同空载电压下铁芯松动与正常情况下的振动信号

提取其在时域、频域、信息域上的特征量

建立振动信号特征量区分能力评估方法并引入线性判别分析（linear discriminant analysis

LDA）投影算法对特征量进行处理

利用支持向量机（support vector machine

SVM）分类器验证经处理得到的特征量的故障识别效果。研究结果表明:变压器运行工况波动下

绝大多数特征量的取值在正常和故障时存在交叠

受负载电流和空载电压的波动影响

区分故障能力较弱;该文建立的区分能力评估方法可筛选出运行工况波动下区分故障能力强的特征量

LDA算法可对存在交叠的特征量进行处理

滤去运行工况的影响。利用额定工况下的振动数据对变压器在运行工况波动下进行故障识别

经筛选和投影处理得到的特征量准确率远高于传统特征量

实现了在运行工况波动下利用变压器振动信号进行故障诊断。文中提出的方法可为变压器在三相不平衡、功率因数多变、谐波等变化工况下利用振动信号诊断变压器故障提供解决思路。

Abstract

In order to use transformer vibration signals to diagnose faults under the fluctuation of operating conditions

the vibration signals of winding in looseness and normal states under different load currents as well as the vibration signals of iron core in looseness and normal states under different no-load voltages are collected

and their characteristic quantities in time domain

frequency domain and information domain are extracted. The evaluation method of distinguishing ability of vibration signal feature quantity is established

and a linear discriminative analysis（LDA） projection algorithm is introduced to process the characteristic quantity. The support vector machine（SVM） classifier is used to verify the fault recognition effect of the feature quantity. The results show that

under the fluctuation of operating conditions

most of characteristic variables have their values overlapped in normal and fault conditions

and the ability to distinguish faults is weak due to the fluctuation of load current and no-load voltage. The method of distinguishing ability established in this paper can be adopted to screen out the characteristic quantities with strong fault distinguishing ability under the fluctuation of operating conditions. The LDA algorithm can be adopted to deal with the characteristic quantity with overlapping and filter out the influence of operation condition. When using the vibration data of rated working condition to identify the fault of transformer under the fluctuation of operating conditions

the accuracy of the characteristic quantity obtained by screening and projection processing is higher than that of the traditional feature quantity

which can diagnose transformer faults under the fluctuation of operating conditions by using the vibration signal. The method proposed in this paper can provide a solution for transformer fault diagnosis by using vibration signals under the fluctuation of operating conditions

such as three-phase unbalance

variable power factor

harmonic and other changing conditions.

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