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Research on Diagnosis and Prediction Method of Stator Interturn Short-Circuit Fault of Traction Motor

Author

Listed:
  • Jianqiang Liu

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Hu Tan

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Yunming Shi

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Yu Ai

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Shaoyong Chen

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Chenyang Zhang

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

Abstract

The traction motor (TM) is an essential part of the high-speed train, the health condition of which determines the quality and safety of the vehicle. Hence, this study proposed a novel approach to diagnosing and predicting the TM stator interturn short-circuit fault (SISCF). Based on the Park vector (PV) of the stator current, this method could overcome the interference of current sensor errors, null shift, and motor frequency fluctuations in the actual conditions. More specifically, Park’s transformation was used to obtain the PV of the stator current. Then, the PV was fitted to obtain the elliptical trajectory and its parameters from which the negative sequence component of the stator current could be calculated. Finally, the SISCF diagnosis and prediction method were realized by the magnitude and trend of the negative current as well as the inclination of the trajectory ellipse. Furthermore, the performance of the proposed method was validated by a simulation model and a series of experiments. The simulation results were consistent with the experimental results, supporting the validity and correctness of the method proposed in this study.

Suggested Citation

  • Jianqiang Liu & Hu Tan & Yunming Shi & Yu Ai & Shaoyong Chen & Chenyang Zhang, 2022. "Research on Diagnosis and Prediction Method of Stator Interturn Short-Circuit Fault of Traction Motor," Energies, MDPI, vol. 15(10), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3759-:d:819755
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    References listed on IDEAS

    as
    1. Maciej Skowron & Teresa Orlowska-Kowalska & Marcin Wolkiewicz & Czeslaw T. Kowalski, 2020. "Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor," Energies, MDPI, vol. 13(6), pages 1-21, March.
    2. Marcin Tomczyk & Ryszard Mielnik & Anna Plichta & Iwona Gołdasz & Maciej Sułowicz, 2021. "Application of Genetic Algorithm for Inter-Turn Short Circuit Detection in Stator Winding of Induction Motor," Energies, MDPI, vol. 14(24), pages 1-20, December.
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