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Identification of the Technical Condition of Induction Motor Groups by the Total Energy Flow

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  • N. I. Koteleva

    (Educational Research Center for Digital Technologies, Saint Petersburg Mining University, 191106 Saint Petersburg, Russia)

  • N. A. Korolev

    (Educational Research Center for Digital Technologies, Saint Petersburg Mining University, 191106 Saint Petersburg, Russia)

  • Y. L. Zhukovskiy

    (Educational Research Center for Digital Technologies, Saint Petersburg Mining University, 191106 Saint Petersburg, Russia)

Abstract

The paper discusses the method of identifying the technical condition of induction motors by classifying the energy data coming from the main common power bus. The work shows the simulation results of induction motor operation. The correlation between occurring defects and current diagrams is presented. The developed simulation model is demonstrated. The general algorithm for conducting experiments is described. Five different experiments to develop an algorithm for the classification are conducted: determination of the motors number in operation with different power; determination of the motors number in operation with equal power; determination of the mode and load of induction electric motor; determination of the fault and its magnitude with regard to operation and load of induction motor; determination of the fault and its magnitude with regard to operation and load of induction motor with regard to non-linear load in the flow. The article also presents an algorithm for preprocessing data to solve the classification problem. In addition, the classification results are shown and recommendations for testing and using the classification algorithm on a real object are made.

Suggested Citation

  • N. I. Koteleva & N. A. Korolev & Y. L. Zhukovskiy, 2021. "Identification of the Technical Condition of Induction Motor Groups by the Total Energy Flow," Energies, MDPI, vol. 14(20), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6677-:d:656696
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    References listed on IDEAS

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    1. Yaroslav Shklyarskiy & Aleksandr Skamyin & Iaroslav Vladimirov & Farit Gazizov, 2020. "Distortion Load Identification Based on the Application of Compensating Devices," Energies, MDPI, vol. 13(6), pages 1-13, March.
    2. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.
    3. Camila Paes Salomon & Claudio Ferreira & Wilson Cesar Sant’Ana & Germano Lambert-Torres & Luiz Eduardo Borges da Silva & Erik Leandro Bonaldi & Levy Ely de Lacerda de Oliveira & Bruno Silva Torres, 2019. "A Study of Fault Diagnosis Based on Electrical Signature Analysis for Synchronous Generators Predictive Maintenance in Bulk Electric Systems," Energies, MDPI, vol. 12(8), pages 1-16, April.
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