基于自主认知深度时间聚类表示的隔离开关故障诊断方法*

doi:10.11985/2024.01.030

电气工程学报 ›› 2024, Vol. 19 ›› Issue (1): 281-289.doi: 10.11985/2024.01.030

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基于自主认知深度时间聚类表示的隔离开关故障诊断方法^*

解骞¹(), 徐浩岚¹(), 王彤²(), 赵发寿³(), 张刚¹(), 党建¹()

1.西安理工大学电气工程学院西安 710054
2.陕西省天然气股份有限公司西安 710016
3.中国石油长庆油田长北作业分公司西安 710018

收稿日期:2023-10-24 修回日期:2023-12-01 出版日期:2024-03-25 发布日期:2024-04-25
通讯作者: 解骞，男，1985年生，博士，讲师。主要研究方向为电力系统/设备态势感知与计算智能技术。E-mail：xieq@xaut.edu.cn
作者简介:徐浩岚，男，2000年生，硕士研究生。主要研究方向为电力设备故障诊断。E-mail：xuhl0929@163.com
王彤，女，1989年生，硕士，工程师。主要研究方向为数据分析与知识发现。E-mail：w.ttong@foxmail.com;
赵发寿，男，1984年生，工程师。主要研究方向为设备诊断及其应用。E-mail：zfs_cq@petrochina.com.cn;
张刚，男，1984年生，博士，副教授。主要研究方向为微网优化配置与调度。E-mail：zhanggang3463003@163.com;
党建，男，1991年生，博士，副教授。主要研究方向为电力设备状态监测与诊断。E-mail：dangjian@xaut.edu.cn
基金资助:
国家自然科学基金资助项目(52009106)

Disconnector Fault Diagnosis Method Based on Autonomous-cognition Deep Temporal Clustering Representation

XIE Qian¹(), XU Haolan¹(), WANG Tong²(), ZHAO Fashou³(), ZHANG Gang¹(), DANG Jian¹()

1. School of Electrical Engineering, Xi’an University of Technology, Xi’an 710054
2. Shaanxi Provincial Natural Gas Company Limited, Xi’an 710016
3. PetroChina Changqing Oilfield Changbei Operation Branch, Xi’an 710018

Received:2023-10-24 Revised:2023-12-01 Online:2024-03-25 Published:2024-04-25

摘要/Abstract

摘要：

为准确识别隔离开关发生的故障，并确定故障类型，保证电网的稳定运行，提出一种基于自主认知的深度时序聚类表示模型(Autonomous-cognition deep temporal clustering representation model，AC-DTCR)对隔离开关的故障进行诊断。在数据量少且类别标签信息不可用的情况下，时间序列聚类是非常好的无监督学习技术，而AC-DTCR模型集成了时间重建和K-means目标，为提高编码器的能力，提出一种假样本生成策略和辅助分类任务，改进集群结构，获得特定于集群的时间表示。根据高压隔离开关故障模拟试验得到的电机电流数据，使用AC-DTCR模型分成四个部分对试验数据进行训练。结果表明，该模型具有良好的分类性能，与传统的分类模型和时间序列聚类模型相比，有更高的准确率，可应用于电力设备故障诊断领域中。

关键词: 深度时序聚类表示, 自注意力机制, 自主认知, 故障诊断, K-means

Abstract:

In order to accurately identify the fault of the disconnector, determine the fault type, and ensure the stable operation of the power grid, an autonomous-cognition deep temporal clustering representation model(AC-DTCR) is proposed to diagnose the fault of the disconnector. In the case of a small amount of data, and the class label information is not available, time series clustering is a very good unsupervised learning technology, and the AC-DTCR model integrates time reconstruction and K-means targets. In order to improve the ability of the encoder, a false sample generation strategy and auxiliary classification task are proposed to improve the cluster structure and obtain a cluster-specific time representation. According to the motor current data obtained from the fault simulation experiment of high voltage disconnector, the AC-DTCR model is divided into four parts to train the experimental data. The results show that the model has good classification performance. Compared with the traditional classification model and time series clustering model, it has higher accuracy and can be applied to the field of power equipment fault diagnosis.

Key words: Deep temporal clustering representation, self-attention, autonomous-cognition, fault diagnosis, K-means

中图分类号:

TM56

解骞, 徐浩岚, 王彤, 赵发寿, 张刚, 党建. 基于自主认知深度时间聚类表示的隔离开关故障诊断方法^*[J]. 电气工程学报, 2024, 19(1): 281-289.

XIE Qian, XU Haolan, WANG Tong, ZHAO Fashou, ZHANG Gang, DANG Jian. Disconnector Fault Diagnosis Method Based on Autonomous-cognition Deep Temporal Clustering Representation[J]. Journal of Electrical Engineering, 2024, 19(1): 281-289.

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基于自主认知深度时间聚类表示的隔离开关故障诊断方法^*

Disconnector Fault Diagnosis Method Based on Autonomous-cognition Deep Temporal Clustering Representation

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