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Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection

Author

Listed:
  • Jing Tang

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

  • Yongheng Yang

    () (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Jie Chen

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

  • Ruichang Qiu

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

  • Zhigang Liu

    () (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
    Beijing Engineering Research Center for Electrical Rail Transit, Beijing 100044, China)

Abstract

Inverter-fed induction motors (IMs) contain a serious of current harmonics, which become severer under stator and rotor faults. The resultant fault components in the currents affect the monitoring of the motor status. With this background, the fault components in the electromagnetic torque under stator faults considering harmonics are derived in this paper, and the fault components in current harmonics under rotor faults are analyzed. More importantly, the monitoring based on the fault characteristics (both in the torque and current) is proposed to provide reliable stator and rotor fault diagnosis. Specifically, the fault components induced by stator faults in the electromagnetic torque are discussed in this paper, and then, fault components are characterized in the torque spectrum to identify stator faults. To achieve so, a full-order flux observer is adopted to calculate the torque. On the other hand, under rotor faults, the sidebands caused by time and space harmonics in the current are analyzed and exploited to recognize rotor faults, being the motor current signature analysis (MCSA). Experimental tests are performed on an inverter-fed 2.2 kW/380 V/50 Hz IM, which verifies the analysis and the effectiveness of the proposed fault diagnosis methods of inverter-fed IMs.

Suggested Citation

  • 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, Open Access Journal, vol. 13(1), pages 1-17, December.
  • Handle: RePEc:gam:jeners:v:13:y:2019:i:1:p:101-:d:301498
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    References listed on IDEAS

    as
    1. Wen Wu & Xuezhi Wu & Jingyuan Yin & Long Jing & Shuai Wang & Jinke Li, 2017. "Characteristic Analysis and Fault-Tolerant Control of Circulating Current for Modular Multilevel Converters under Sub-Module Faults," Energies, MDPI, Open Access Journal, vol. 10(11), pages 1-22, November.
    2. Pedro Gonçalves & Sérgio Cruz & André Mendes, 2019. "Finite Control Set Model Predictive Control of Six-Phase Asymmetrical Machines—An Overview," Energies, MDPI, Open Access Journal, vol. 12(24), pages 1-42, December.
    3. 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, Open Access Journal, vol. 12(14), pages 1-15, July.
    4. Mitja Nemec & Vanja Ambrožič & Rastko Fišer & David Nedeljković & Klemen Drobnič, 2019. "Induction Motor Broken Rotor Bar Detection Based on Rotor Flux Angle Monitoring," Energies, MDPI, Open Access Journal, vol. 12(5), pages 1-17, February.
    5. Zia Ullah & Jin Hur, 2018. "A Comprehensive Review of Winding Short Circuit Fault and Irreversible Demagnetization Fault Detection in PM Type Machines," Energies, MDPI, Open Access Journal, vol. 11(12), pages 1-27, November.
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    9. Caixia Gao & Yanjie Nie & Jikai Si & Ziyi Fu & Haichao Feng, 2019. "Mode Recognition and Fault Positioning of Permanent Magnet Demagnetization for PMSM," Energies, MDPI, Open Access Journal, vol. 12(9), pages 1-14, April.
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    More about this item

    Keywords

    characteristics analysis; fault detection; stator fault; rotor fault; torque estimation; induction motor;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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