IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i2p296-d1314514.html
   My bibliography  Save this article

Fault Detection and Prediction for Power Transformers Using Fuzzy Logic and Neural Networks

Author

Listed:
  • Balduíno César Mateus

    (Research Centre in Asset Management and Systems Engineering, RCM 2+ Lusófona University, Campo Grande, 376, 1749-024 Lisboa, Portugal
    CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, 62001-001 Covilhã, Portugal)

  • José Torres Farinha

    (Instituto Superior de Engenharia de Coimbra, Polytechnic Institute of Coimbra, RCM 2+ Research Centre in Asset Management and System Engineering, 3030-199 Coimbra, Portugal
    Centre for Mechanical Engineering, Materials and Processes—CEMMPRE, University of Coimbra, 3030-788 Coimbra, Portugal)

  • Mateus Mendes

    (Instituto Superior de Engenharia de Coimbra, Polytechnic Institute of Coimbra, RCM 2+ Research Centre in Asset Management and System Engineering, 3030-199 Coimbra, Portugal
    Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal)

Abstract

Transformers are indispensable in the industry sector and society in general, as they play an important role in power distribution, allowing the delivery of electricity to different loads and locations. Because of their great importance, it is necessary that they have high reliability, so that their failure does not cause additional losses to the companies. Inside a transformer, the primary and secondary turns are insulated by oil. Analyzing oil samples, it is possible to diagnose the health status or type of fault in the transformer. This paper combines Fuzzy Logic and Neural Network techniques, with the main objective of detecting and if possible predicting failures, so that the maintenance technicians can make decisions and take action at the right time. The results showed an accuracy of up to 95% in detecting failures. This study also highlights the importance of predictive maintenance and provides a unique approach to support decision-making for maintenance technicians.

Suggested Citation

  • Balduíno César Mateus & José Torres Farinha & Mateus Mendes, 2024. "Fault Detection and Prediction for Power Transformers Using Fuzzy Logic and Neural Networks," Energies, MDPI, vol. 17(2), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:296-:d:1314514
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/2/296/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/2/296/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nurkamilya Daurenbayeva & Almas Nurlanuly & Lyazzat Atymtayeva & Mateus Mendes, 2023. "Survey of Applications of Machine Learning for Fault Detection, Diagnosis and Prediction in Microclimate Control Systems," Energies, MDPI, vol. 16(8), pages 1-21, April.
    2. Alexandre Martins & Balduíno Mateus & Inácio Fonseca & José Torres Farinha & João Rodrigues & Mateus Mendes & António Marques Cardoso, 2023. "Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models," Energies, MDPI, vol. 16(6), pages 1-26, March.
    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.
    1. Ruiqi Tian & Santiago Gomez-Rosero & Miriam A. M. Capretz, 2023. "Health Prognostics Classification with Autoencoders for Predictive Maintenance of HVAC Systems," Energies, MDPI, vol. 16(20), pages 1-21, October.

    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:17:y:2024:i:2:p:296-:d:1314514. 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.