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

Chen Cao, Zheng Li (), Jialin Wang, Jiayu Zhang, Ying Li and Qingli Wang
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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

Energies, 2025, vol. 18, issue 4, 1-19

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.

Keywords: transformer core; fault diagnosis; vibration waveform; joint algorithm; loosening fault (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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