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Digital Identification Algorithms for Primary Frequency Control in Unified Power System

Author

Listed:
  • Natalia Bakhtadze

    (V.A. Trapeznikov Institute of Control Sciences, 65, Profsoyuznaya, 117997 Moscow, Russia)

  • Evgeny Maximov

    (V.A. Trapeznikov Institute of Control Sciences, 65, Profsoyuznaya, 117997 Moscow, Russia)

  • Natalia Maximova

    (V.A. Trapeznikov Institute of Control Sciences, 65, Profsoyuznaya, 117997 Moscow, Russia)

Abstract

The article studies and develops the methods for assessing the degree of participation of power plants in the general primary frequency control in a unified energy system (UES) of Russia based on time series analysis of frequency and power. To identify the processes under study, methods of associative search are proposed. The methods are based on process knowledgebase development, data mining, associative research, and inductive learning. Real-time identification models generated using these algorithms can be used in automatic control and decision support systems. Evaluation of the behavior of individual UES members enables timely prevention of abnormal and emergency situations. Methods for predictive diagnostics of generating equipment in terms of their readiness to participate in the primary frequency control are also proposed. In view of the non-stationarity of the load in electrical networks, the algorithms have been developed using wavelet analysis. Case studies are given showing the operating of the proposed methods.

Suggested Citation

  • Natalia Bakhtadze & Evgeny Maximov & Natalia Maximova, 2021. "Digital Identification Algorithms for Primary Frequency Control in Unified Power System," Mathematics, MDPI, vol. 9(22), pages 1-17, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2875-:d:677422
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    References listed on IDEAS

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    1. Andrey Rylov & Pavel Ilyushin & Aleksandr Kulikov & Konstantin Suslov, 2021. "Testing Photovoltaic Power Plants for Participation in General Primary Frequency Control under Various Topology and Operating Conditions," Energies, MDPI, vol. 14(16), pages 1-20, August.
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    Cited by:

    1. Emad M. Ahmed & Ali Selim & Hammad Alnuman & Waleed Alhosaini & Mokhtar Aly & Emad A. Mohamed, 2022. "Modified Frequency Regulator Based on TI λ -TD μ FF Controller for Interconnected Microgrids with Incorporating Hybrid Renewable Energy Sources," Mathematics, MDPI, vol. 11(1), pages 1-39, December.
    2. Andrey I. Vlasov & Boris V. Artemiev & Kirill V. Selivanov & Kirill S. Mironov & Jasur O. Isroilov, 2022. "Predictive Control Algorithm for A Variable Load Hybrid Power System on the Basis of Power Output Forecast," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 1-7, May.

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