IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2023i1p220-d1311177.html
   My bibliography  Save this article

OLTC Fault detection Based on Acoustic Emission and Supported by Machine Learning

Author

Listed:
  • Andrzej Cichoń

    (Department of Electric Power Engineering and Renewable Energy, Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland)

  • Michał Włodarz

    (Department of Electric Power Engineering and Renewable Energy, Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland)

Abstract

Power transformers are an essential part of the power grid. They have a relatively low rate of failure, but removing the consequences is costly when it occurs. One of the elements of power transformers that are often the reason for shutting down the unit is the on-load tap changer (OLTC). Many methods have been developed to assess the technical condition of OLTCs. However, they require the transformer to be taken out of service for the duration of the diagnostics, or they do not enable precise diagnostics. Acoustic emission (AE) signals are widely used in industrial diagnostics. The generated signals are difficult to interpret for complex systems, so artificial intelligence tools are becoming more widely used to simplify the diagnostic process. This article presents the results of research on the possibility of creating an online OLTC diagnostics method based on AE signals. An extensive measurement database containing many frequently occurring OLTC defects was created for this research. A method of feature extraction from AE signals based on wavelet decomposition was developed. Several machine learning models were created to select the most effective one for classifying OLTC defects. The presented method achieved 96% efficiency in OLTC defect classification.

Suggested Citation

  • Andrzej Cichoń & Michał Włodarz, 2023. "OLTC Fault detection Based on Acoustic Emission and Supported by Machine Learning," Energies, MDPI, vol. 17(1), pages 1-14, December.
  • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:220-:d:1311177
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/1/220/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/1/220/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ponomarenko, Alexey & Tatarintsev, Stas, 2023. "Incorporating financial development indicators into early warning systems," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    2. Zbigniew Nadolny, 2023. "Design and Optimization of Power Transformer Diagnostics," Energies, MDPI, vol. 16(18), pages 1-7, September.
    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. James McNamara & Elizabeth J. Z. Robinson & Katharine Abernethy & Donald Midoko Iponga & Hannah N. K. Sackey & Juliet H. Wright & EJ Milner-Gulland, 2020. "COVID-19, Systemic Crisis, and Possible Implications for the Wild Meat Trade in Sub-Saharan Africa," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 1045-1066, August.
    2. Nguyen, Hoai Thi Thanh & Tram, Huong Thi Xuan & Nguyen, Linh Thi Thuy, 2023. "Interest rates and systemic risk:Evidence from the Vietnamese economy," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).

    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:2023:i:1:p:220-:d:1311177. 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.