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Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents

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  • Przemyslaw Pietrzak

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

  • Marcin Wolkiewicz

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

Abstract

Stator winding faults are one of the most common faults of permanent magnet synchronous motors (PMSMs), and searching for methods to efficiently detect this type of fault and at an early stage of damage is still an ongoing, important topic. This paper deals with the selected methods for detecting stator winding faults (short-circuits) of a permanent magnet synchronous motor, which are based on the analysis of the stator phase current signal. These methods were experimentally verified and their effectiveness was carefully compared. The article presents the results of experimental studies obtained from the spectral analysis of the stator phase current, stator phase current envelope, and the discrete wavelet transform. The original fault indicators (FIs) based on the observation of the symptoms of stator winding fault were distinguished using the aforementioned methods, which clearly show which symptom is most sensitive to the incipient fault of the stator winding of PMSMs.

Suggested Citation

  • Przemyslaw Pietrzak & Marcin Wolkiewicz, 2021. "Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents," Energies, MDPI, vol. 14(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1630-:d:517098
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    References listed on IDEAS

    as
    1. Zhang, Jian & Tounzi, Abdelmounaim & Benabou, Abdelkader & Le Menach, Yvonnick, 2021. "Detection of magnetization loss in a PMSM with Hilbert Huang transform applied to non-invasive search coil voltage," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 184-195.
    2. Maciej Skowron & Teresa Orlowska-Kowalska & Marcin Wolkiewicz & Czeslaw T. Kowalski, 2020. "Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor," Energies, MDPI, vol. 13(6), pages 1-21, March.
    3. Grzegorz Tarchała & Marcin Wolkiewicz, 2019. "Performance of the Stator Winding Fault Diagnosis in Sensorless Induction Motor Drive," Energies, MDPI, vol. 12(8), pages 1-20, April.
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    Cited by:

    1. Rafael de Farias Campos & Cesar da Silva Liberato & José de Oliveira & Tiago Jackson May Dezuo & Ademir Nied, 2022. "Dynamic Strategy for Effective Current Reduction in Brushless DC Synchronous Motors Fault Tolerant Operation," Energies, MDPI, vol. 15(24), pages 1-17, December.
    2. Rodolfo V. Rocha & Renato M. Monaro, 2023. "Algorithm for Fast Detection of Stator Turn Faultsin Variable-Speed Synchronous Generators," Energies, MDPI, vol. 16(5), pages 1-23, March.
    3. Karolina Kudelina & Bilal Asad & Toomas Vaimann & Anton Rassõlkin & Ants Kallaste & Huynh Van Khang, 2021. "Methods of Condition Monitoring and Fault Detection for Electrical Machines," Energies, MDPI, vol. 14(22), pages 1-20, November.
    4. Attallah, Omneya & Ibrahim, Rania A. & Zakzouk, Nahla E., 2023. "CAD system for inter-turn fault diagnosis of offshore wind turbines via multi-CNNs & feature selection," Renewable Energy, Elsevier, vol. 203(C), pages 870-880.
    5. Kamila Jankowska & Mateusz Dybkowski, 2021. "A Current Sensor Fault Tolerant Control Strategy for PMSM Drive Systems Based on C ri Markers," Energies, MDPI, vol. 14(12), pages 1-18, June.

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