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Substantiation of a Rational Model of an Induction Motor in a Predictive Energy-Efficient Control System

Author

Listed:
  • Grygorii Diachenko

    (Department of Electric Drive, Dnipro University of Technology, 19 Yavornytskoho Ave., 49005 Dnipro, Ukraine)

  • Ivan Laktionov

    (Department of Computer Systems Software, Dnipro University of Technology, 19 Yavornytskoho Ave., 49005 Dnipro, Ukraine)

  • Dariusz Sala

    (Faculty of Management, AGH University of Krakow, 30 Mickiewicza Ave., 30-059 Krakow, Poland)

  • Michał Pyzalski

    (Faculty of Management, AGH University of Krakow, 30 Mickiewicza Ave., 30-059 Krakow, Poland)

  • Oleksandr Balakhontsev

    (Department of Electric Drive, Dnipro University of Technology, 19 Yavornytskoho Ave., 49005 Dnipro, Ukraine)

  • Yuliya Pazynich

    (Faculty of Management, AGH University of Krakow, 30 Mickiewicza Ave., 30-059 Krakow, Poland
    Department of Philosophy and Pedagogy, Dnipro University of Technology, 19 Yavornytskoho Ave., 49005 Dnipro, Ukraine)

Abstract

The development and implementation of scientifically substantiated solutions for the improvement and modernization of electromechanical devices, systems, and complexes, including electric drives, is an urgent theoretical and applied task for energetics, industry, transport, and other key areas, both in global and national contexts. The aim of this paper is to identify a rational model of an induction motor that balances computational simplicity and control system performance based on predictive approaches while ensuring maximum energy efficiency and reference tracking during the operation in dynamic modes. Five main mathematical models of an induction machine with different levels of detail have been selected. Three predictive control models have been implemented using GRAMPC (v 2.2), Matlab MPC Toolbox (v 24.1), and fmincon (R2024a) (from Matlab Optimization Toolbox). It has been established that in the dynamic mode of operation, the equivalent induction motor circuit with parameters R f e = c o n s t , L μ = f I 1 d , and T F = f ( ω R m ) is the most appropriate in terms of the following criteria: accuracy of control action generation, computation speed, and calculation of energy consumption.

Suggested Citation

  • Grygorii Diachenko & Ivan Laktionov & Dariusz Sala & Michał Pyzalski & Oleksandr Balakhontsev & Yuliya Pazynich, 2025. "Substantiation of a Rational Model of an Induction Motor in a Predictive Energy-Efficient Control System," Energies, MDPI, vol. 18(17), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:17:p:4628-:d:1738486
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    References listed on IDEAS

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    1. Valeriy Nikolsky & Roman Dychkovskyi & Edgar Cáceres Cabana & Natalia Howaniec & Bartłomiej Jura & Katarzyna Widera & Adam Smoliński, 2022. "The Hydrodynamics of Translational−Rotational Motion of Incompressible Gas Flow within the Working Space of a Vortex Heat Generator," Energies, MDPI, vol. 15(4), pages 1-14, February.
    2. Qais Ali & Maria Luisa Di Silvestre & Pio Alessandro Lombardi & Eleonora Riva Sanseverino & Gaetano Zizzo, 2024. "Electrifying the Road to Net-Zero: Implications of Electric Vehicles and Carbon Emission Coefficient Factors in European Power Systems," Sustainability, MDPI, vol. 16(12), pages 1-22, June.
    3. Viktor Rjabtšikov & Anton Rassõlkin & Karolina Kudelina & Ants Kallaste & Toomas Vaimann, 2023. "Review of Electric Vehicle Testing Procedures for Digital Twin Development: A Comprehensive Analysis," Energies, MDPI, vol. 16(19), pages 1-17, October.
    4. Oleksandr Vladyko & Dmytro Maltsev & Łukasz Gliwiński & Roman Dychkovskyi & Kinga Stecuła & Artur Dyczko, 2025. "Enhancing Mining Enterprise Energy Resource Extraction Efficiency Through Technology Synthesis and Performance Indicator Development," Energies, MDPI, vol. 18(7), pages 1-15, March.
    5. Ihor Blinov & Virginijus Radziukynas & Pavlo Shymaniuk & Artur Dyczko & Kinga Stecuła & Viktoriia Sychova & Volodymyr Miroshnyk & Roman Dychkovskyi, 2025. "Smart Management of Energy Losses in Distribution Networks Using Deep Neural Networks," Energies, MDPI, vol. 18(12), pages 1-17, June.
    6. Małgorzata Magdziarczyk & Andrzej Chmiela & Roman Dychkovskyi & Adam Smoliński, 2024. "The Cost Reduction Analysis of Green Hydrogen Production from Coal Mine Underground Water for Circular Economy," Energies, MDPI, vol. 17(10), pages 1-12, May.
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