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Detection of Inter-Turn Faults in Multi-Phase Ferrite-PM Assisted Synchronous Reluctance Machines

Author

Listed:
  • Carlos Candelo-Zuluaga

    (Electrical Engineering Department, Universitat Politècnica de Catalunya, 08222 Terrassa, Spain)

  • Jordi-Roger Riba

    (Electrical Engineering Department, Universitat Politècnica de Catalunya, 08222 Terrassa, Spain)

  • Carlos López-Torres

    (Electrical Engineering Department, Universitat Politècnica de Catalunya, 08222 Terrassa, Spain)

  • Antoni Garcia

    (Electrical Engineering Department, Universitat Politècnica de Catalunya, 08222 Terrassa, Spain)

Abstract

Inter-turn winding faults in five-phase ferrite-permanent magnet-assisted synchronous reluctance motors (fPMa-SynRMs) can lead to catastrophic consequences if not detected in a timely manner, since they can quickly progress into more severe short-circuit faults, such as coil-to-coil, phase-to-ground or phase-to-phase faults. This paper analyzes the feasibility of detecting such harmful faults in their early stage, with only one short-circuited turn, since there is a lack of works related to this topic in multi-phase fPMa-SynRMs. Two methods are tested for this purpose, the analysis of the spectral content of the zero-sequence voltage component (ZSVC) and the analysis of the stator current spectra, also known as motor current signature analysis (MCSA), which is a well-known fault diagnosis method. This paper compares the performance and sensitivity of both methods under different operating conditions. It is proven that inter-turn faults can be detected in the early stage, with the ZSVC providing more sensitivity than the MCSA method. It is also proven that the working conditions have little effect on the sensitivity of both methods. To conclude, this paper proposes two inter-turn fault indicators and the threshold values to detect such faults in the early stage, which are calculated from the spectral information of the ZSVC and the line currents.

Suggested Citation

  • Carlos Candelo-Zuluaga & Jordi-Roger Riba & Carlos López-Torres & Antoni Garcia, 2019. "Detection of Inter-Turn Faults in Multi-Phase Ferrite-PM Assisted Synchronous Reluctance Machines," Energies, MDPI, vol. 12(14), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2733-:d:249142
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    References listed on IDEAS

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    1. Luqman Maraaba & Zakariya Al-Hamouz & Mohammad Abido, 2018. "An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors," Energies, MDPI, vol. 11(3), pages 1-18, March.
    2. Riba, Jordi-Roger & López-Torres, Carlos & Romeral, Luís & Garcia, Antoni, 2016. "Rare-earth-free propulsion motors for electric vehicles: A technology review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 367-379.
    3. Yucai Wu & Guanhua Ma, 2019. "Anti-Interference and Location Performance for Turn-to-Turn Short Circuit Detection in Turbo-Generator Rotor Windings," Energies, MDPI, vol. 12(7), pages 1-18, April.
    4. Hong-Chan Chang & Yu-Ming Jheng & Cheng-Chien Kuo & Yu-Min Hsueh, 2019. "Induction Motors Condition Monitoring System with Fault Diagnosis Using a Hybrid Approach," Energies, MDPI, vol. 12(8), pages 1-12, April.
    5. Baoshan Huang & Guojin Feng & Xiaoli Tang & James Xi Gu & Guanghua Xu & Robert Cattley & Fengshou Gu & Andrew D. Ball, 2019. "A Performance Evaluation of Two Bispectrum Analysis Methods Applied to Electrical Current Signals for Monitoring Induction Motor-Driven Systems," Energies, MDPI, vol. 12(8), pages 1-23, April.
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    Cited by:

    1. Tanvir Alam Shifat & Rubiya Yasmin & Jang-Wook Hur, 2021. "A Data Driven RUL Estimation Framework of Electric Motor Using Deep Electrical Feature Learning from Current Harmonics and Apparent Power," Energies, MDPI, vol. 14(11), pages 1-21, May.
    2. Carlos Candelo-Zuluaga & Jordi-Roger Riba & Dinesh V. Thangamuthu & Antoni Garcia, 2020. "Detection of Partial Demagnetization Faults in Five-Phase Permanent Magnet Assisted Synchronous Reluctance Machines," Energies, MDPI, vol. 13(13), pages 1-17, July.
    3. Jing Tang & Yongheng Yang & Jie Chen & Ruichang Qiu & Zhigang Liu, 2019. "Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection," Energies, MDPI, vol. 13(1), pages 1-17, December.

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