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

Author

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  • 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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
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