Skip to main content

TECHNOLOGY AND CODE article

Front. Energy Res.
Sec. Smart Grids
Volume 12 - 2024 | doi: 10.3389/fenrg.2024.1443570
This article is part of the Research Topic Advancements in Power System Condition Monitoring, Fault Diagnosis and Environmental Compatibility View all 5 articles

Busbar Fault Diagnosis Method Based on Multi-Source Information Fusion

Provisionally accepted
Xuebao Jiang Xuebao Jiang 1*Haiou Cao Haiou Cao 2Chenbin Zhou Chenbin Zhou 1*Xuchao Ren Xuchao Ren 2*Jiaoxiao Shen Jiaoxiao Shen 1*Jiayan Yu Jiayan Yu 1*
  • 1 State Grid Suzhou Power Supply Company, Suzhou, Liaoning Province, China
  • 2 State Grid Jiangsu Electric Power Co., LTD, Nanjing, China

The final, formatted version of the article will be published soon.

    Against the backdrop of smart grid development, the electric power system demands higher accuracy and comprehensiveness in fault analysis. Establishing a digital twin platform for multiple equipment faults represents the future direction of power system development. Presently, while many researchers employ artificial intelligence algorithms to diagnose faults in key equipment such as transmission lines and transformers, intelligent diagnostic methods for busbar faults remain insufficient. Therefore, this paper proposes a busbar fault diagnosis method based on multi-source information fusion. Initially, the diagnostic method for busbar faults is explored, conducting both time-domain and frequency-domain analyses on simulated fault data. The data of this model are optimized using DS evidence theory to enhance algorithm training speed. Subsequently, BP neural network training is implemented.Finally, validation testing of fault data demonstrates a fault recognition accuracy of 99.1% for this method.Experimental results illustrate the method's feasibility and low computational costs, thereby advancing the development of digital twin platforms for power system fault diagnosis.

    Keywords: information fusion, Busbar fault, time-domain analysis, Frequency-domain analysis, Neural Network

    Received: 04 Jun 2024; Accepted: 31 Jul 2024.

    Copyright: © 2024 Jiang, Cao, Zhou, Ren, Shen and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Xuebao Jiang, State Grid Suzhou Power Supply Company, Suzhou, Liaoning Province, China
    Chenbin Zhou, State Grid Suzhou Power Supply Company, Suzhou, Liaoning Province, China
    Xuchao Ren, State Grid Jiangsu Electric Power Co., LTD, Nanjing, China
    Jiaoxiao Shen, State Grid Suzhou Power Supply Company, Suzhou, Liaoning Province, China
    Jiayan Yu, State Grid Suzhou Power Supply Company, Suzhou, Liaoning Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.