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Global Sliding-Mode Suspension Control of Bearingless Switched Reluctance Motor under Eccentric Faults to Increase Reliability of Motor

Author

Listed:
  • Pulivarthi Nageswara Rao

    (Department of Electrical Electronics and Communication Engineering, GITAM (Deemed to be University), Visakhapatnam 530045, India)

  • Ramesh Devarapalli

    (Department of Electrical Engineering, IIT (ISM), Dhanbad 826004, India)

  • Fausto Pedro García Márquez

    (Ingenium Research Group, University of Castilla-La Mancha, 13071 Ciudad Real, Spain)

  • Hasmat Malik

    (BEARS, University Town, NUS Campus, Singapore 138602, Singapore)

Abstract

Bearingless motor development is a substitute for magnetic bearing motors owing to several benefits, such as nominal repairs, compactness, lower cost, and no need for high-power amplifiers. Compared to conventional motors, rotor levitation and its steady control is an additional duty in bearingless switched reluctance motors when starting. For high-speed applications, the use of simple proportional integral derivative and fuzzy control schemes are not in effect in suspension control of the rotor owing to inherent parameter variations and external suspension loads. In this paper, a new robust global sliding-mode controller is suggested to control rotor displacements and their positions to ensure fewer eccentric rotor displacements when a bearingless switched reluctance motor is subjected to different parameter variations and loads. Extra exponential fast-decaying nonlinear functions and rotor-tracking error functions have been used in the modeling of the global sliding-mode switching surface. Simulation studies have been conducted under different testing conditions. From the results, it is shown that rotor displacements and suspension forces in X and Y directions are robust and stable. Owing to the proposed control action of the suspension phase currents, the rotor always comes back rapidly to the center position under any uncertainty.

Suggested Citation

  • Pulivarthi Nageswara Rao & Ramesh Devarapalli & Fausto Pedro García Márquez & Hasmat Malik, 2020. "Global Sliding-Mode Suspension Control of Bearingless Switched Reluctance Motor under Eccentric Faults to Increase Reliability of Motor," Energies, MDPI, vol. 13(20), pages 1-38, October.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5485-:d:431753
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    References listed on IDEAS

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    1. Md Liton Hossain & Ahmed Abu-Siada & S. M. Muyeen, 2018. "Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review," Energies, MDPI, vol. 11(5), pages 1-14, May.
    2. Pierre Tchakoua & René Wamkeue & Mohand Ouhrouche & Fouad Slaoui-Hasnaoui & Tommy Andy Tameghe & Gabriel Ekemb, 2014. "Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges," Energies, MDPI, vol. 7(4), pages 1-36, April.
    3. Ahmed Chaibet & Moussa Boukhnifer & Nadir Ouddah & Eric Monmasson, 2020. "Experimental Sensorless Control of Switched Reluctance Motor for Electrical Powertrain System," Energies, MDPI, vol. 13(12), pages 1-15, June.
    4. Pinar Pérez, Jesús María & García Márquez, Fausto Pedro & Tobias, Andrew & Papaelias, Mayorkinos, 2013. "Wind turbine reliability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 463-472.
    5. Hui Cai & Hui Wang & Mengqiu Li & Shiqi Shen & Yaojing Feng & Jian Zheng, 2018. "Torque Ripple Reduction for Switched Reluctance Motor with Optimized PWM Control Strategy," Energies, MDPI, vol. 11(11), pages 1-27, November.
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    Cited by:

    1. Si-Woo Song & Won-Ho Kim & Ju Lee & Dong-Hoon Jung, 2023. "A Study on Weight Reduction and High Performance in Separated Magnetic Bearings," Energies, MDPI, vol. 16(7), pages 1-13, March.

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