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Eccentricity fault detection – From induction machines to DFIG—A review

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  • Faiz, Jawad
  • Moosavi, S.M.M.

Abstract

Induction machines (IMs) are widely used in different applications. Unpredicted breakdown of these machines usually leads to costly downtimes and repairs. These expenses can be minimized using proper condition monitoring techniques. Eccentricity fault is one of the widespread faults causing machine malfunction; its detection could be useful for prevention of harmful consequences. In this paper, different works on eccentricity diagnosis of IMs with different types of supply have been reviewed. It commences from the simplest open-loop machine, and by considering torque variation and inverter switching gradually turns to complicated closed-loop machine with different control strategies. While in most cases, current is used for condition monitoring, in some instances power and voltage are employed for fault diagnosis. Due to extensive use of IMs in wind turbines as doubly-fed induction generator (DFIG), in addition to declaration of importance of eccentricity fault diagnosis in DFIG, detection of eccentricity fault in DFIG is also reviewed.

Suggested Citation

  • Faiz, Jawad & Moosavi, S.M.M., 2016. "Eccentricity fault detection – From induction machines to DFIG—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 169-179.
    • Handle: RePEc:eee:rensus:v:55:y:2016:i:c:p:169-179
    DOI: 10.1016/j.rser.2015.10.113
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    References listed on IDEAS

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    1. Amirat, Y. & Benbouzid, M.E.H. & Al-Ahmar, E. & Bensaker, B. & Turri, S., 2009. "A brief status on condition monitoring and fault diagnosis in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2629-2636, December.
    2. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
    3. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
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    Cited by:

    1. Annalisa Santolamazza & Daniele Dadi & Vito Introna, 2021. "A Data-Mining Approach for Wind Turbine Fault Detection Based on SCADA Data Analysis Using Artificial Neural Networks," Energies, MDPI, vol. 14(7), pages 1-25, March.
    2. Zijian Liu & Pinjia Zhang & Shan He & Jin Huang, 2021. "A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines," Energies, MDPI, vol. 14(14), pages 1-21, July.
    3. Fausto Pedro García Márquez & Alberto Pliego Marugán & Jesús María Pinar Pérez & Stuart Hillmansen & Mayorkinos Papaelias, 2017. "Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines," Energies, MDPI, vol. 10(8), pages 1-19, July.
    4. Jordi Burriel-Valencia & Ruben Puche-Panadero & Javier Martinez-Roman & Angel Sapena-Baño & Martin Riera-Guasp & Manuel Pineda-Sánchez, 2019. "Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines," Energies, MDPI, vol. 12(17), pages 1-18, August.

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