东北大学学报(自然科学版) ›› 2025, Vol. 46 ›› Issue (7): 22-29.DOI: 10.12068/j.issn.1005-3026.2025.20240218

• 工业智能理论与方法 • 上一篇    下一篇

基于工业智能的电力变压器数字孪生故障诊断方法

冯健1(), 张博闻1,2, 赵宁1, 江辉杰1   

  1. 1.东北大学 信息科学与工程学院,辽宁 沈阳 110819
    2.国家电网有限公司 东北分部,辽宁 沈阳 110000
  • 收稿日期:2024-11-25 出版日期:2025-07-15 发布日期:2025-09-24
  • 通讯作者: 冯健
  • 基金资助:
    国家自然科学基金资助项目(U22A2055)

Digital Twin Fault Diagnosis Method of Power Transformer Based on Industrial Intelligence

Jian FENG1(), Bo-wen ZHANG1,2, Ning ZHAO1, Hui-jie JIANG1   

  1. 1.School of Information Science & Engineering,Northeastern University,Shenyang 110819,China
    2.Northeast Branch,State Grid Corporation of China,Shenyang 110000,China.
  • Received:2024-11-25 Online:2025-07-15 Published:2025-09-24
  • Contact: Jian FENG

摘要:

作为新一代信息技术与先进制造技术融合的关键发展方向,工业智能利用智能化、数字化和自动化手段,能够显著提升工业生产效率并优化工业设备的预测和维护管理.本文聚焦于工业设备的智能化,以电力变压器在电力系统中高效、稳定运行的需求为出发点,基于电磁场方程和等效电路模型,构建了基于数字孪生技术的变压器匝间短路故障模型.该模型从电磁场角度对比分析变压器在正常状态和故障状态下的对称性,实现了数字孪生技术与故障诊断的结合.此外,通过对变压器虚拟模型的深入分析,实现对发生故障位置的定位,确保变压器的安全运行,提升其可靠性与效率,从而推动整个电力系统的智能化与现代化进程.

关键词: 数字孪生, 工业智能, 电力变压器, 电磁场, 故障定位

Abstract:

As a key development direction integrating new-generation information technology with advanced manufacturing techniques, industrial intelligence leverages intelligent, digital, and automated methods to significantly enhance industrial production efficiency and optimize the prediction and maintenance management of industrial equipment. This paper focuses on the intelligentization of industrial equipment, with the goal of ensuring the efficient and stable operation of power transformers within power systems. A digital twin model for transformer inter-turn short circuit faults is constructed based on electromagnetic field equations and equivalent circuit models. The model analyzes the symmetry of the transformer in both normal and fault conditions from an electromagnetic field perspective, thereby integrating digital twin technology with fault diagnosis. Furthermore, through in-depth analysis of the virtual model of the transformer, the location of faults is accurately identified, ensuring the safe operation of the transformer, improving its reliability and efficiency, and advancing the intelligentization and modernization of the entire power system.

Key words: digital twin (DT), industrial intelligence, power transformer, electromagnetic field, fault location

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