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Research on Simulation Analysis and Joint Diagnosis Algorithm of Transformer Core-Loosening Faults Based on Vibration Characteristics

Author

Listed:
  • Chen Cao

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Zheng Li

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Jialin Wang

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Jiayu Zhang

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Ying Li

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Qingli Wang

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

Abstract

The existing methods for transformer core-loosening fault diagnosis primarily focus on fundamental frequency analysis, neglecting higher-frequency components, which limits early detection accuracy. This study proposes a comprehensive approach integrating full-band vibration analysis, including high-order harmonics, to enhance diagnostic precision. A theoretical model coupling magnetostriction and thermodynamics was developed, combined with empirical mode decomposition (EMD) and Pearson’s correlation coefficient for fault characterization. A 10 kV transformer core vibration test platform was constructed, capturing signals under normal, partially loose, and completely loose states. The simulation results aligned with the experimental data, showing vibration accelerations of 0.01 m/s 2 (Phase A) and 0.023 m/s 2 (Phase B). A multi-physics coupling model incorporating Young’s modulus variations simulated core loosening, revealing increased high-frequency components (up to 1000 Hz) and vibration amplitudes (0.2757 m/s 2 for complete loosening). The joint EMD–Pearson method quantified fault severity, yielding correlation values of 0.0007 (normal), 0.0044 (partial loosening), and 0.0116 (complete loosening), demonstrating a clear positive correlation with fault progression. Experimental validation confirmed the model’s reliability, with the simulations matching the test results. This approach addresses the limitations of traditional methods by incorporating high-frequency analysis and multi-physics modeling, significantly improving early fault detection accuracy and providing a quantifiable diagnostic framework for transformer core health monitoring.

Suggested Citation

  • Chen Cao & Zheng Li & Jialin Wang & Jiayu Zhang & Ying Li & Qingli Wang, 2025. "Research on Simulation Analysis and Joint Diagnosis Algorithm of Transformer Core-Loosening Faults Based on Vibration Characteristics," Energies, MDPI, vol. 18(4), pages 1-19, February.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:914-:d:1590859
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    References listed on IDEAS

    as
    1. Shuming Zhang & Hong Zhou, 2024. "Transformer Fault Diagnosis Based on Multi-Strategy Enhanced Dung Beetle Algorithm and Optimized SVM," Energies, MDPI, vol. 17(24), pages 1-15, December.
    2. Xiaowen Wu & Ling Li & Nianguang Zhou & Ling Lu & Sheng Hu & Hao Cao & Zhiqiang He, 2018. "Diagnosis of DC Bias in Power Transformers Using Vibration Feature Extraction and a Pattern Recognition Method," Energies, MDPI, vol. 11(7), pages 1-20, July.
    Full references (including those not matched with items on IDEAS)

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