IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i2p377-d1568907.html
   My bibliography  Save this article

AI-Driven Signal Processing for SF6 Circuit Breaker Performance Optimization

Author

Listed:
  • Philippe A. V. D. Liz

    (Laboratory of Robotics, Intelligent and Complex Systems—ROBSIC, Itabira 35903-087, MG, Brazil
    Current address: Institute of Technological Sciences, ICT, Federal University of Itajubá, UNIFEI, Itabira Campus, Itabira 35903-087, MG, Brazil.
    These authors contributed equally to this work.)

  • Giovani B. Vitor

    (Laboratory of Robotics, Intelligent and Complex Systems—ROBSIC, Itabira 35903-087, MG, Brazil
    These authors contributed equally to this work.)

  • Ricardo T. Lima

    (Centrais Elétricas Brasileiras S/A—ELETROBRÁS, Rio de Janeiro 20091-005, RJ, Brazil
    These authors contributed equally to this work.)

  • Aurélio L. M. Coelho

    (Laboratory of Robotics, Intelligent and Complex Systems—ROBSIC, Itabira 35903-087, MG, Brazil
    These authors contributed equally to this work.)

  • Eben P. Silveira

    (Laboratory of Robotics, Intelligent and Complex Systems—ROBSIC, Itabira 35903-087, MG, Brazil
    These authors contributed equally to this work.)

Abstract

This work presents an approach based on signal processing and artificial intelligence (AI) to identify the pre-insertion resistor (PIR) and main contact instants during the operation of high-voltage SF6 circuit breakers to help improve the settings of controlled switching and attenuate transients. For this, the current and voltage signals of a real Brazilian substation are used as AI inputs, considering the noise and interferences common in this type of environment. Thus, the proposed modeling considers the signal preprocessing steps for feature extraction, the generation of the dataset for model training, the use of different machine learning techniques to automatically find the desired points, and, finally, the identification of the best moments for controlled switching of the circuit breakers. As a result, the models evaluated obtained good performance in the identification of operation points above 93%, considering precision and accuracy. In addition, valuable statistical notes related to the controlled switching condition are obtained from the circuit breakers evaluated in this research.

Suggested Citation

  • Philippe A. V. D. Liz & Giovani B. Vitor & Ricardo T. Lima & Aurélio L. M. Coelho & Eben P. Silveira, 2025. "AI-Driven Signal Processing for SF6 Circuit Breaker Performance Optimization," Energies, MDPI, vol. 18(2), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:377-:d:1568907
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/2/377/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/2/377/
    Download Restriction: no
    ---><---

    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:jeners:v:18:y:2025:i:2:p:377-:d:1568907. 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: 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.