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Self-Correcting Virtual Current Sensor Based on the Modified Luenberger Observer for Fault-Tolerant Induction Motor Drive

Author

Listed:
  • Michal Adamczyk

    (Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland)

  • Teresa Orlowska-Kowalska

    (Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland)

Abstract

Fault-tolerant control (FTC) solutions are increasingly being used in modern drive systems with AC motors. Such systems provide a higher degree of security and solutions that allow the on-line detection and localization of failures, as well as the switching of the control mode to a mode that allows us to continue the operation or safely stop the drive system. As the current sensors (CSs) are necessary to ensure precise control of the AC motors, in the event of their failure, one of two strategies can be used—hardware or software redundancy. The first strategy requires the use of additional measuring sensors. For this reason, the algorithmic solution, based on the Luenberger Observer (LO), has been proposed in this article as one of the software redundancy methods. In contrast to methods presented in the literature, the proposed solution allows one not only to compensate the stator current in a phase with a faulty CS, but also to adjust the correction of current estimation based on a measured signal in the other phase with a healthy CS. Extensive simulation studies in the direct rotor flux-oriented control (DRFOC) structure with the induction motor (IM) confirm the effectiveness of the proposed method. In addition, the proposed solution allows the drive system to be controlled even if all CSs are damaged.

Suggested Citation

  • Michal Adamczyk & Teresa Orlowska-Kowalska, 2021. "Self-Correcting Virtual Current Sensor Based on the Modified Luenberger Observer for Fault-Tolerant Induction Motor Drive," Energies, MDPI, vol. 14(20), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6767-:d:658496
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    References listed on IDEAS

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    1. Mohammed Obaid Mustafa & George Nikolakopoulos & Thomas Gustafsson, 2014. "Faults Classification Scheme for Three Phase Induction Motor," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 3(1), pages 1-20, January.
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    Cited by:

    1. Maciej Skowron & Krystian Teler & Michal Adamczyk & Teresa Orlowska-Kowalska, 2022. "Classification of Single Current Sensor Failures in Fault-Tolerant Induction Motor Drive Using Neural Network Approach," Energies, MDPI, vol. 15(18), pages 1-17, September.
    2. Usha Sengamalai & T. M. Thamizh Thentral & Palanisamy Ramasamy & Mohit Bajaj & Syed Sabir Hussain Bukhari & Ehab E. Elattar & Ahmed Althobaiti & Salah Kamel, 2022. "Mitigation of Circulating Bearing Current in Induction Motor Drive Using Modified ANN Based MRAS for Traction Application," Mathematics, MDPI, vol. 10(8), pages 1-24, April.

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