IDEAS home Printed from https://ideas.repec.org/a/epw/ejece0/v7y2023i1id19490.html

Angle Instability and Oscillations Control using SVC: A Deep Reinforcement Learning Enhanced Local Controller

Author

Listed:
  • Edin Huseinbasic

    (High Vocational School “Hasan Kikic”, Bosnia and Herzegovina)

Abstract

An advanced control scheme to deal with transient angle instability and low-frequency angle oscillations in power systems is proposed in this paper. The control combines an existing controller (derived from the concept of Lyapunov energy functions) and deep Q-network (a deep reinforcement learning algorithm) to control a static VAr compensator. This control is modified in this paper in a way to directly control thyristor controller reactor part of the compensator, offering an easier implementation. The aim is to improve performances of existing control through the use of deep Q-network while retaining its basic system stabilization characteristics. Advantages of the proposed control scheme are illustrated by implementation of a model of four-machine test power system (this system is considered as the benchmark when studying the phenomena dealt with in this paper) in MATLAB/Simulink environment and using TensorFlow toolkit for deep Q-network usage.

Suggested Citation

  • Edin Huseinbasic, 2023. "Angle Instability and Oscillations Control using SVC: A Deep Reinforcement Learning Enhanced Local Controller," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 7(1), pages 74-78, January.
    • Handle: RePEc:epw:ejece0:v:7:y:2023:i:1:id:19490
    DOI: 10.24018/ejece.2023.7.1.490
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejece/article/view/19490
    File Function: Abstract page
    Download Restriction: no
    File URL: https://eu-opensci.org/index.php/ejece/article/download/19490/11269
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.24018/ejece.2023.7.1.490?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:epw:ejece0:v:7:y:2023:i:1:id:19490. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: support (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejece .

    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.