IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i11p1644-d1400813.html
   My bibliography  Save this article

Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine

Author

Listed:
  • Mihail Senyuk

    (Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia)

  • Svetlana Beryozkina

    (College of Engineering and Technology, American University of the Middle East, Kuwait)

  • Murodbek Safaraliev

    (Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia)

  • Muhammad Nadeem

    (College of Engineering and Technology, American University of the Middle East, Kuwait)

  • Ismoil Odinaev

    (Department of Automated Electrical Systems, Ural Federal University, 620002 Yekaterinburg, Russia)

  • Firuz Kamalov

    (Department of Electrical Engineering, Canadian University Dubai, Dubai 117781, United Arab Emirates)

Abstract

This study presents the results of the development and testing of a methodology for selecting parameters of the characteristics of fast valving in a steam turbine for emergency power system management to maintain dynamic stability based on machine learning algorithms. Modern power systems have reduced inertia and increased stochasticity due to the active integration of renewable energy sources. As a result, there is an increased likelihood of incorrect operation in traditional emergency automation devices, developed on the principles of deterministic analysis of transient processes. To date, it is possible to increase the adaptability and accuracy of emergency power system management through the application of machine learning algorithms. In this work, fast valving in a steam turbine was chosen as the considered device of emergency automation. To form the data sample, the IEEE39 mathematical model was used, for which benchmark laws of change in the position of the cutoff valve during the fast valving of a steam turbine were selected. The considered machine learning algorithms for classifying the law of change in the position of the steam turbine’s cutoff valve, k-nearest neighbors, support vector machine, decision tree, random forest, and extreme gradient boosting were used. The results show that the highest accuracy corresponds to extreme gradient boosting. For the selected eXtreme Gradient Boosting algorithm, the classification accuracy on the training set was 98.17%, and on the test set it was 97.14%. The work also proposes a methodology for forming synthetic data for the use of machine learning algorithms for emergency management of power systems and suggests directions for further research.

Suggested Citation

  • Mihail Senyuk & Svetlana Beryozkina & Murodbek Safaraliev & Muhammad Nadeem & Ismoil Odinaev & Firuz Kamalov, 2024. "Methodology for Transient Stability Enhancement of Power Systems Based on Machine Learning Algorithms and Fast Valving in a Steam Turbine," Mathematics, MDPI, vol. 12(11), pages 1-19, May.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:11:p:1644-:d:1400813
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/11/1644/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/11/1644/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nikolay Ruban & Anton Kievets & Mikhail Andreev & Aleksey Suvorov, 2023. "Turbine Fast Valving Setting Method Based on the Hybrid Simulation Approach," Energies, MDPI, vol. 16(4), pages 1-24, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:11:p:1644-:d:1400813. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.