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A New Effective Method of Induction Machine Condition Assessment Based on Zero-Sequence Voltage (ZSV) Symptoms

Author

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  • Arkadiusz Duda

    (Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24 Street, 31-155 Cracow, Poland)

  • Maciej Sułowicz

    (Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24 Street, 31-155 Cracow, Poland)

Abstract

Non-invasive diagnostic methods for electric machines’ diagnostics, which can be used during their operation in a drive system, are needed in many branches of the production industry. For the reliable condition assessment of electric machines, especially those operating in drive systems, various tools and methods have been suggested. One diagnostic method that has not been fully recognized and documented is a diagnostic method based on zero-sequence voltage component (ZSV) applications for the condition assessment of induction machines. In this paper, the application of ZSV in induction machine diagnostics is proposed. A factor that speaks in favor of applying this signal in such diagnostics is the high sensitivity of the signal to damage occurrence, and the distinct change of extracted symptoms in the case of asymmetry. It is possible to obtain a high signal amplitude, which simplifies its processing and the elaboration of reliable diagnostic factors. This ZSV-based method is also able to be applied to big machines used in industry. Due to the saturation effects visible in the ZSV signal, new diagnostic symptoms can appear, which allows for an easier condition assessment of certain machines. The usefulness of the described diagnostic method in machine condition assessment was shown through an equivalent circuit modeling process, finite element analysis, and laboratory tests of the machine.

Suggested Citation

  • Arkadiusz Duda & Maciej Sułowicz, 2020. "A New Effective Method of Induction Machine Condition Assessment Based on Zero-Sequence Voltage (ZSV) Symptoms," Energies, MDPI, vol. 13(14), pages 1-26, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3544-:d:382390
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    References listed on IDEAS

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    1. Xueping Xu & Qinkai Han & Fulei Chu, 2018. "Review of Electromagnetic Vibration in Electrical Machines," Energies, MDPI, vol. 11(7), pages 1-33, July.
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    Cited by:

    1. Janusz Petryna & Arkadiusz Duda & Maciej Sułowicz, 2021. "Eccentricity in Induction Machines—A Useful Tool for Assessing Its Level," Energies, MDPI, vol. 14(7), pages 1-26, April.
    2. Marcin Tomczyk & Ryszard Mielnik & Anna Plichta & Iwona Gołdasz & Maciej Sułowicz, 2021. "Application of Genetic Algorithm for Inter-Turn Short Circuit Detection in Stator Winding of Induction Motor," Energies, MDPI, vol. 14(24), pages 1-20, December.
    3. Arkadiusz Duda & Piotr Drozdowski, 2020. "Induction Motor Fault Diagnosis Based on Zero-Sequence Current Analysis," Energies, MDPI, vol. 13(24), pages 1-25, December.

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