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ORIGINAL RESEARCH article

Front. Signal Process.
Sec. Systems Health Diagnosis and Prognosis
Volume 4 - 2024 | doi: 10.3389/frsip.2024.1433831

Inter-turn Short Circuit Diagnosis of Wound-Field Doubly Salient Machine Using Multi-Signal Fusion and GA-XGBoost

Provisionally accepted
Ran Chen Ran Chen 1Chong Shen Chong Shen 2*Tianming Sheng Tianming Sheng 3Yao Zhao Yao Zhao 3*
  • 1 State Grid Shanghai Municipal Electric Power Company, Shanghai, China
  • 2 State Grid Hangzhou Power Supply Company, Hangzhou, China
  • 3 Shanghai University of Electric Power, Shanghai, Shanghai Municipality, China

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

    The Wound-Field Doubly Salient Machine (WFDSM) is a core assembly of generating system.Condition monitoring and early fault diagnosis of WFDSM are key to improving system reliability.In this paper, a fault diagnosis method based on multi-signal mixed domain fusion at the feature level and Genetic Algorithm improved XGBoost (GA-XGBoost) is proposed. Firstly, low-pass noise reduction, singular value decomposition noise reduction, and other signal pre-processing are applied to the current and vibration signals of the early inter-turn short circuit faults. Secondly, the time domain, frequency domain, and entropy features of the current signal, along with the time domain features of the vibration signal, are extracted, together forming a diagnostic feature set. Then the feature set is put into the GA-XGBoost model. Finally, the results show that the proposed method of feature fusion achieves an accuracy of 99.3%. Thus, the multi-signal mixed domain fusion has stronger signal characteristic expression ability. Also, the GA-XGBoost model achieves better generalization ability and higher accuracy in the small-scale samples of WFDSM faults. The experimental results demonstrate that this method can diagnose various conditions effectively and also has strong anti-interference capability under extreme conditions.

    Keywords: Wound-Field Doubly Salient Machine, Fault diagnosis, extreme conditions, signal fusion, XGBoost

    Received: 16 May 2024; Accepted: 29 Nov 2024.

    Copyright: © 2024 Chen, Shen, Sheng and Zhao. 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:
    Chong Shen, State Grid Hangzhou Power Supply Company, Hangzhou, China
    Yao Zhao, Shanghai University of Electric Power, Shanghai, 130012, Shanghai Municipality, 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.