IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2022i1p201-d1014213.html
   My bibliography  Save this article

Classification of Partial Discharges Recorded by the Method Using the Phenomenon of Scintillation

Author

Listed:
  • Aleksandra Płużek

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

  • Łukasz Nagi

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

Abstract

Classification is one of the most common methods of supervised learning, which is divided into a process of data acquisition, data mining, feature analysis, machine learning algorithm selection, model learning and validation, as well as prediction of the result, which was done in the current work. The data that were analyzed concerned ionizing radiation signals generated by partial discharges, recorded by a method using the phenomenon of scintillation. It was decided to check if the data could be classified and if it was possible to determine the defect of an electrical power device. It was possible to find out which classifier (algorithm) worked best for the task, and that the data obtained can be classified, as well as that it is possible to determine the defect. In addition, it was possible to check what effect changing the default values of the classifier’s parameters has on the effectiveness of classification.

Suggested Citation

  • Aleksandra Płużek & Łukasz Nagi, 2022. "Classification of Partial Discharges Recorded by the Method Using the Phenomenon of Scintillation," Energies, MDPI, vol. 16(1), pages 1-9, December.
  • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:201-:d:1014213
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/201/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/1/201/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jianfeng Zheng & Zhichao Chen & Qun Wang & Hao Qiang & Weiyue Xu, 2022. "GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network," Energies, MDPI, vol. 15(19), pages 1-14, October.
    2. Daria Wotzka & Wojciech Sikorski & Cyprian Szymczak, 2022. "Investigating the Capability of PD-Type Recognition Based on UHF Signals Recorded with Different Antennas Using Supervised Machine Learning," Energies, MDPI, vol. 15(9), pages 1-20, April.
    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. Guang Wang & Jiale Xie & Shunli Wang, 2023. "Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis," Energies, MDPI, vol. 16(14), pages 1-3, July.
    2. Songyuan Li & Pengxian Song & Zhanpeng Wei & Xu Li & Qinghua Tang & Zhengzheng Meng & Ji Li & Songtao Liu & Yuhuai Wang & Jin Li, 2022. "Partial Discharge Detection and Defect Location Method in GIS Cable Terminal," Energies, MDPI, vol. 16(1), pages 1-10, December.
    3. Zbigniew Nadolny, 2022. "Impact of Changes in Limit Values of Electric and Magnetic Field on Personnel Performing Diagnostics of Transformers," Energies, MDPI, vol. 15(19), pages 1-15, 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:16:y:2022:i:1:p:201-:d:1014213. 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.