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Digital Twin Formation Method for Distributed Generation Plants of Cyber–Physical Power Supply Systems

Author

Listed:
  • Yuri Bulatov

    (Department of Energy, Bratsk State University, 665730 Bratsk, Russia)

  • Andrey Kryukov

    (Department of Power Supply and Electrical Engineering, Irkutsk National Research Technical University, 664074 Irkutsk, Russia)

  • Andrey Batuhtin

    (Department of Energy, Transbaikal State University, 672039 Chita, Russia)

  • Konstantin Suslov

    (Department of Power Supply and Electrical Engineering, Irkutsk National Research Technical University, 664074 Irkutsk, Russia)

  • Ksenia Korotkova

    (Department of Energy, Bratsk State University, 665730 Bratsk, Russia)

  • Denis Sidorov

    (Department of Applied Mathematics, Energy Systems Institute of Siberian Branch of Russian Academy of Sciences, 664033 Irkutsk, Russia
    Industrial Mathematics Laboratory, Baikal School of BRICS, Irkutsk National Research Technical University, 664074 Irkutsk, Russia)

Abstract

The purpose of the study presented in the article was to develop a method for the formation of digital twins for distributed generation plants operating as part of cyber–physical power supply systems. A method of forming a digital twin for a system for automatic regulation of the voltage and rotor speed of a synchronous generator is considered. The structure of a digital twin is presented in the form of a multiply connected model using experimental data. The possibility of using a fuzzy inference system, artificial neural networks, and a genetic algorithm for solving the problem is shown. As a result of the research, neuro-fuzzy models of the elements of the distributed generation plant were obtained, which are an integral part of the digital twin. Based on the simulation results, the following conclusions were drawn: the proposed method for constructing an optimized fuzzy model gives acceptable results when compared with experimental data and shows practical applicability in constructing a digital twin. In the future, in order to simplify the model, it is necessary to solve the problem of optimizing the number of rules in the fuzzy inference system. It is also advisable to direct further research to the formation of a complete hierarchical fuzzy system that connects all elements of the digital twin.

Suggested Citation

  • Yuri Bulatov & Andrey Kryukov & Andrey Batuhtin & Konstantin Suslov & Ksenia Korotkova & Denis Sidorov, 2022. "Digital Twin Formation Method for Distributed Generation Plants of Cyber–Physical Power Supply Systems," Mathematics, MDPI, vol. 10(16), pages 1-19, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2886-:d:886461
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    References listed on IDEAS

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    1. Ingo Liere-Netheler & Frank Schuldt & Karsten von Maydell & Carsten Agert, 2020. "Simulation of Incidental Distributed Generation Curtailment to Maximize the Integration of Renewable Energy Generation in Power Systems," Energies, MDPI, vol. 13(16), pages 1-22, August.
    2. Chang, Da-Yong, 1996. "Applications of the extent analysis method on fuzzy AHP," European Journal of Operational Research, Elsevier, vol. 95(3), pages 649-655, December.
    3. Fang Liu & Ranran Li & Aliona Dreglea, 2019. "Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model," Energies, MDPI, vol. 12(18), pages 1-16, September.
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    Cited by:

    1. Denis Sidorov, 2023. "Preface to “Model Predictive Control and Optimization for Cyber-Physical Systems”," Mathematics, MDPI, vol. 11(4), pages 1-3, February.

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