基于卷积神经网络的变压器有载分接开关故障识别

曾全昊; 王丰华; 郑一鸣; 何文林

doi:10.7500/AEPS20191108003

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基于卷积神经网络的变压器有载分接开关故障识别

研制与开发 | 更新时间：2025-06-16

- 基于卷积神经网络的变压器有载分接开关故障识别
- Fault Recognition of On-load Tap-changer in Power Transformer Based on Convolutional Neural Network
- 电力系统自动化-方正平台 2020年44卷第11期页码：144-151
- 作者机构：
  
  1.上海交通大学电子信息与电气工程学院，上海市 200240
  2.国网浙江省电力有限公司电力科学研究院，浙江省杭州市 310014
- 作者简介：
  
  曾全昊(1995—)，男，硕士研究生，主要研究方向：电力设备智能故障诊断技术。E-mail：qhzengee@163.com
  王丰华(1973—)，女，通信作者，博士，副教授，主要研究方向：电力设备状态监测与智能化、交直流设备振声机理与机械故障诊断、电力系统接地技术等。E-mail：fhwang7723@sjtu.edu.cn
  郑一鸣(1987—)，男，博士，高级工程师，主要研究方向：电力系统过电压与接地技术、变电设备状态检修和运维等。E-mail：yiming_zheng@yeah.net
- 基金信息：
  
  国家重点研发计划资助项目(2017YFB0902700);国网浙江省电力有限公司科技项目(52111DS160022)
- DOI：10.7500/AEPS20191108003
  中图分类号：
- 收稿日期：2019-11-08，
  
  修回日期：2020-02-10，
  
  网络出版日期：2020-04-03，
  
  纸质出版日期：2020-06-10
- 稿件说明：
移动端阅览
曾全昊,王丰华,郑一鸣等.基于卷积神经网络的变压器有载分接开关故障识别[J].电力系统自动化,2020,44(11):144-151.

ZENG Quanhao,WANG Fenghua,ZHENG Yiming,et al.Fault Recognition of On-load Tap-changer in Power Transformer Based on Convolutional Neural Network[J].Automation of Electric Power Systems,2020,44(11):144-151.
曾全昊,王丰华,郑一鸣等.基于卷积神经网络的变压器有载分接开关故障识别[J].电力系统自动化,2020,44(11):144-151. DOI： 10.7500/AEPS20191108003.

ZENG Quanhao,WANG Fenghua,ZHENG Yiming,et al.Fault Recognition of On-load Tap-changer in Power Transformer Based on Convolutional Neural Network[J].Automation of Electric Power Systems,2020,44(11):144-151. DOI： 10.7500/AEPS20191108003.

摘要

为进一步提高变压器有载分接开关(OLTC)故障识别的精度，从OLTC切换过程中振动信号递归图的纹理特征出发，提出了一种基于卷积神经网络(CNN)的变压器OLTC故障识别方法。首先根据OLTC振动信号的相空间分布，基于相点距离映射构建了OLTC振动信号的距离映射递归图(DMRP)，然后通过合理选取CNN的网络层数、卷积核尺寸等结构超参数和对卷积核进行降维处理，提出了基于CNN的OLTC故障识别模型。对某CM型OLTC正常与典型故障下振动信号的计算结果表明，DMRP能自适应地对振动信号的相空间相点分布进行描述，所提出的识别模型对OLTC的典型故障均具有良好的识别性能，尤其在轻微故障的识别上相比于现有方法准确率提升了至少10%。

Abstract

To further improve the fault recognition accuracy of on-load tap-changer (OLTC) of power transformer

this paper presents a fault recognition method of OLTC based on convolutional neural network (CNN) considering the texture features of recurrence plot for vibration signal during the switching process of OLTC. The distance mapping recurrence plot (DMRP) is constructed by using the phase point distance mapping according to the phase space distribution of vibration signals for OLTC. Then a CNN based OLTC fault recognition model is proposed by reasonably selecting the structure hyper-parameters of CNN

including the number of network layers and convolution kernel size

and reducing the convolution kernel dimensionality. The calculation results of vibration signals from a CM type OLTC under normal and typical mechanical fault conditions show that the DMRP can adaptively describe the point distribution of the vibration signals in phase space. The recognition model has excellent recognition performance on OLTC typical faults. Especially

the recognition rate of slight fault is improved by at least 10% compared with the existing methods.

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references

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