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Application of Genetic Algorithm for Inter-Turn Short Circuit Detection in Stator Winding of Induction Motor

Author

Listed:
  • Marcin Tomczyk

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

  • Ryszard Mielnik

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

  • Anna Plichta

    (Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24 Str., 31-155 Cracow, Poland)

  • Iwona Gołdasz

    (Faculty of Environmental and Power Engineering, Cracow University of Technology, Warszawska 24 Str., 31-155 Cracow, Poland)

  • Maciej Sułowicz

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

Abstract

This paper presents a new method of inter-turn short-circuit detection in cage induction motors. The method is based on experimental data recorded during load changes. Measured signals were analyzed using a genetic algorithm. This algorithm was next used in the diagnostics procedure. The correctness of fault detection was verified during experimental tests for various configurations of inter-turn short-circuits. The tests were run for several relevant diagnostic signals that contain symptoms of faults in an examined cage induction motor. The proposed algorithm of inter-turn short-circuit detection for various levels of winding damage and for various loads of the examined motor allows one to state the usefulness of this diagnostic method in normal industry conditions of motor exploitation.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8523-:d:704855
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    References listed on IDEAS

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    1. Jan Maciej Kościelny & Michał Syfert & Paweł Wnuk, 2021. "Diagnostic Row Reasoning Method Based on Multiple-Valued Evaluation of Residuals and Elementary Symptoms Sequence," Energies, MDPI, vol. 14(9), pages 1-18, April.
    2. 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.
    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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    Cited by:

    1. Jianqiang Liu & Hu Tan & Yunming Shi & Yu Ai & Shaoyong Chen & Chenyang Zhang, 2022. "Research on Diagnosis and Prediction Method of Stator Interturn Short-Circuit Fault of Traction Motor," Energies, MDPI, vol. 15(10), pages 1-17, May.

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