考虑特征耦合的Bi-LSTM变压器故障诊断方法

doi:10.11930/j.issn.1004-9649.202209055

中国电力 ›› 2023, Vol. 56 ›› Issue (3): 100-108,117.DOI: 10.11930/j.issn.1004-9649.202209055

考虑特征耦合的Bi-LSTM变压器故障诊断方法

李刚^1,2, 孟坤¹, 贺帅¹, 刘云鹏³, 杨宁⁴

1. 华北电力大学计算机系，河北保定 071003;
2. 复杂能源系统智能计算教育部工程研究中心，河北保定 071003;
3. 华北电力大学电力工程系，河北保定 071003;
4. 中国电力科学研究院有限公司，北京 100192

收稿日期:2022-09-15 修回日期:2022-12-05 出版日期:2023-03-28 发布日期:2023-03-28
作者简介:李刚(1980-),男,通信作者,博士,副教授,从事电力人工智能、能源区块链、故障预测与健康管理研究,E-mail:ququ_er2003@126.com;孟坤(1997-),男,硕士研究生,从事电力人工智能、大数据挖掘研究,E-mail:mengsjtl@163.com
基金资助:
国家电网有限公司科技项目（基于物联网技术的变电智能设备分级标准、关键技术研究及试点应用， 5500-202055096A-0-0-00）。

A Bi-LSTM-Based Transformer Fault Diagnosis Method Considering Feature Coupling

LI Gang^1,2, MENG Kun¹, HE Shuai¹, LIU Yunpeng³, YANG Ning⁴

1. Department of Computer, North China Electric Power University, Baoding 071003, China;
2. Engineering Research Center of Intelligent Computing for Complex Energy Systems, Baoding 071003, China;
3. Department of Electric Engineering, North China Electric Power University, Baoding 071003, China;
4. China Electric Power Research Institute, Beijing 100192, China

Received:2022-09-15 Revised:2022-12-05 Online:2023-03-28 Published:2023-03-28
Supported by:
This work is supported by Science and Technology Program of SGCC (Classification Standard, Key Technology Research and Pilot Application of Substation Intelligent Equipment Based on Internet of Things Technology, No.5500-202055096A-0-0-00).

摘要/Abstract

摘要： 电力变压器是保障电力系统安全稳定运行的关键设备之一，而现有故障诊断方法未能充分挖掘设备内部的特征作用关系，对运行状态变化的敏感性较低，在故障诊断准确性和可靠性提升上具有一定的限制。针对上述问题，提出一种考虑特征耦合的双向长短期记忆网络变压器故障诊断方法。首先，根据变压器运行机理确定初始特征状态转移序列；然后，在此基础上构建考虑复杂依赖关系的深度神经网络故障诊断模型，挖掘特征之间的耦合关系，并进行精细化状态评估；最后，通过算例仿真实验验证先验特征序列对故障诊断模型的支撑作用。所提方法提升了故障诊断效果，为电力设备智能化、精细化的运维需求提供了可参考的方案。

关键词: 电力变压器, 故障诊断, 运行机理, 特征状态转移序列, 特征耦合

Abstract: Power transformer is one of the key equipment to ensure the safe and stable operation of the power system, but the existing fault diagnosis methods cannot fully exploit the feature interaction within the equipment and have poor sensitivity to the changes of operating conditions, which has limited the improvement of fault diagnosis accuracy and reliability. To address the above problems, a transformer fault diagnosis method is proposed based on bi-directional long short-term memory (Bi-LSTM) considering feature coupling. Firstly, the initial transition sequence of feature state is determined based on the equipment operation mechanism; then, a deep neural network fault diagnosis model is constructed with consideration of complex dependencies to mine the feature coupling relationship for refined condition assessment; finally, the simulation results have verified the support role of the priori feature sequence for the fault diagnosis model. The proposed method improves the fault diagnosis effectiveness, and can provide a reference solution for intelligent and refined maintenance of power equipment.

Key words: power transformer, fault diagnosis, operation mechanism, transition sequence of feature state, feature coupling

李刚, 孟坤, 贺帅, 刘云鹏, 杨宁. 考虑特征耦合的Bi-LSTM变压器故障诊断方法[J]. 中国电力, 2023, 56(3): 100-108,117.

LI Gang, MENG Kun, HE Shuai, LIU Yunpeng, YANG Ning. A Bi-LSTM-Based Transformer Fault Diagnosis Method Considering Feature Coupling[J]. Electric Power, 2023, 56(3): 100-108,117.

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https://www.electricpower.com.cn/CN/Y2023/V56/I3/100

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考虑特征耦合的Bi-LSTM变压器故障诊断方法

A Bi-LSTM-Based Transformer Fault Diagnosis Method Considering Feature Coupling

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考虑特征耦合的Bi-LSTM变压器故障诊断方法

A Bi-LSTM-Based Transformer Fault Diagnosis Method Considering Feature Coupling

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