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An Improved Method for Discerning Broken Rotor Bar Fault and Load Oscillation in Induction Motors

Author

Listed:
  • Liling Sun

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Boqiang Xu

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

Abstract

A few methods for discerning broken rotor bar (BRB) fault and load oscillation in induction motors have been reported in the literature. However, they all perhaps inevitably fail in adverse cases in which these two phenomena are simultaneously present. To tackle this problem, an improved method for discerning BRB fault and load oscillation is proposed in this paper based on the following work. On the one hand, the theoretical basis is analytically extended to include such an adverse case, yielding some important findings on the spectra of the instantaneous reactive and active powers. A novel strategy is thus outlined to correctly discern BRB fault and load oscillation even when simultaneously present. On the other hand, Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) is adopted as the spectral analysis technique to deal with the instantaneous reactive and active powers, yielding a certain improvement compared to the existing methods, adopting Fast Fourier Transform (FFT). Simulation and experimental results demonstrate that the proposed method can correctly discern BRB fault and load oscillation even when simultaneously present.

Suggested Citation

  • Liling Sun & Boqiang Xu, 2018. "An Improved Method for Discerning Broken Rotor Bar Fault and Load Oscillation in Induction Motors," Energies, MDPI, vol. 11(11), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3130-:d:182355
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    Citations

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    Cited by:

    1. Xinyue Liu & Yan Yan & Kaibo Hu & Shan Zhang & Hongjie Li & Zhen Zhang & Tingna Shi, 2022. "Fault Diagnosis of Rotor Broken Bar in Induction Motor Based on Successive Variational Mode Decomposition," Energies, MDPI, vol. 15(3), pages 1-16, February.
    2. 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.
    3. 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, vol. 12(5), pages 1-17, February.
    4. Petr Kacor & Petr Bernat & Petr Moldrik, 2021. "Utilization of Two Sensors in Offline Diagnosis of Squirrel-Cage Rotors of Asynchronous Motors," Energies, MDPI, vol. 14(20), pages 1-23, October.

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