Fault Diagnosis of Transformer in Electric Energy Metering System Based on PCA-ELM

WANG Xin; YOU Zezhang; DAI Tingrong; CAO Min; LI Xiang; ZHAO Yanfeng; WANG Xianpei

doi:10.13296/j.1001-1609.hva.2019.10.030

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Fault Diagnosis of Transformer in Electric Energy Metering System Based on PCA-ELM

更新时间：2025-04-26

- Fault Diagnosis of Transformer in Electric Energy Metering System Based on PCA-ELM
- High VoltageApparatus Vol. 55, Issue 10, Pages: 182-188(2019)
- 作者机构：
  
  1. 南方电网电能计量重点实验室
  2. 云南电网有限责任公司电力科学研究院
  3. 武汉大学电子信息学院
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2019.10.030
  CLC： TM45
- Published：2019
- 稿件说明：
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[1]王昕,尤泽樟,代婷荣,曹敏,李翔,赵艳峰,王先培.基于PCA-ELM算法的电能计量互感器故障诊断[J].高压电器,2019,55(10):182-188.

WANG Xin, YOU Zezhang, DAI Tingrong, et al. Fault Diagnosis of Transformer in Electric Energy Metering System Based on PCA-ELM[J]. High voltageapparatus, 2019, 55(10): 182-188.
[1]王昕,尤泽樟,代婷荣,曹敏,李翔,赵艳峰,王先培.基于PCA-ELM算法的电能计量互感器故障诊断[J].高压电器,2019,55(10):182-188. DOI： 10.13296/j.1001-1609.hva.2019.10.030.

WANG Xin, YOU Zezhang, DAI Tingrong, et al. Fault Diagnosis of Transformer in Electric Energy Metering System Based on PCA-ELM[J]. High voltageapparatus, 2019, 55(10): 182-188. DOI： 10.13296/j.1001-1609.hva.2019.10.030.

摘要

为了解决电能计量互感器的故障诊断算法稳定性不足的问题

首先对电能计量互感器电路信号理论进行分析

在此基础上

提出了PCA-ELM算法（主成分分析法和极端学习机算法结合的算法）

并应用到电能计量互感器故障诊断中。由于ELM具有能以极快的速度获得很好的泛化性能的优势

将其与PCA算法结合

可以提高算法的稳定性。选择电能计量互感器的8个主要信号作为ELM的输入值、使用标号No.1-10指代互感器处于正常工作状态和9种主要故障类型

作为输出

对样本进行训练。训练完成后选取8种故障类型做验证测试

测试结果显示

运用PCA-ELM模型算法对电能计量系统互感器故障进行诊断

能得到很好的故障类型辨识和诊断效果。

Abstract

On the basis of the theoretical analysis of the fault of the electric energy metering transformer

the PCAELM model is applied to the fault diagnosis of electronic transformer in metering system. Using the advantage of obtaining good generalization performance at a very fast speed

ELM can be used in transformer fault diagnosis. We pick up 8 operating parameters of electric energy metering system as input to ELM. The normal state of the transformer and the nine main kinds of fault types are expressed by the label No.1-10. The samples are collected to train ELM

and eight cases are selected to test. The results show that the PCA-ELM model has a good effect on the fault diagnosis of transformer faults.

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