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Investigating the Capability of PD-Type Recognition Based on UHF Signals Recorded with Different Antennas Using Supervised Machine Learning

Author

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  • Daria Wotzka

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

  • Wojciech Sikorski

    (Institute of Electric Power Engineering, Poznan University of Technology, 60-965 Poznan, Poland)

  • Cyprian Szymczak

    (Institute of Electric Power Engineering, Poznan University of Technology, 60-965 Poznan, Poland)

Abstract

The article presents research on the influence of the type of UHF antenna and the type of machine learning algorithm on the effectiveness of classification of partial discharges (PD) occurring in the insulation system of a power transformer. For this purpose, four antennas specially adapted to be installed in the transformer tank (UHF disk sensor, UHF drain valve sensor, planar inverted F-type antenna, Hilbert curve fractal antenna) and a reference log-periodic antenna were used in laboratory tests. During the research, the main types of PD, typical for oil-paper insulation, were generated, i.e., PD in oil, PD in oil wedge, PD in gas bubbles, surface discharges, and creeping sparks. For the registered UHF PD pulses, nine features in the frequency domain and four features in the wavelet domain were extracted. Then, the PD classification process was carried out with the use of selected methods of supervised machine learning. The study investigated the influence of the number and type of feature on the obtained classification results gained with the following machine-learning methods: decision tree, support vector machine, Bayes method, k-nearest neighbor, linear discriminant, and ensemble machine. As a result of the works carried out, it was found that the highest accuracies are gathered for the feature representing peak frequency using a decision tree, reaching values, depending on the type of antenna, from 89.7% to 100%, with an average of 96.8%. In addition, it was found that the MRMR method reduces the number of features from 13 to 1 while maintaining very high effectiveness. The broadband log-periodic antenna ensured the highest average efficiency (100%) in the PD classification. In the case of the tested antennas adapted to work in an energy transformer tank, the highest defect-recognition efficiency is provided by the UHF disk sensor (99.3%), and the lowest (89.7%) is by the UHF drain valve sensor.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3167-:d:802748
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    References listed on IDEAS

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    1. Wojciech Sikorski & Krzysztof Walczak & Wieslaw Gil & Cyprian Szymczak, 2020. "On-Line Partial Discharge Monitoring System for Power Transformers Based on the Simultaneous Detection of High Frequency, Ultra-High Frequency, and Acoustic Emission Signals," Energies, MDPI, vol. 13(12), pages 1-37, June.
    2. Hamidreza Karami & Farzane Askari & Farhad Rachidi & Marcos Rubinstein & Wojciech Sikorski, 2022. "An Inverse-Filter-Based Method to Locate Partial Discharge Sources in Power Transformers," Energies, MDPI, vol. 15(6), pages 1-21, March.
    3. Jian Li & Xudong Li & Lin Du & Min Cao & Guochao Qian, 2016. "An Intelligent Sensor for the Ultra-High-Frequency Partial Discharge Online Monitoring of Power Transformers," Energies, MDPI, vol. 9(5), pages 1-15, May.
    4. Michał Kozioł & Łukasz Nagi & Michał Kunicki & Ireneusz Urbaniec, 2019. "Radiation in the Optical and UHF Range Emitted by Partial Discharges," Energies, MDPI, vol. 12(22), pages 1-16, November.
    5. Minh-Tuan Nguyen & Viet-Hung Nguyen & Suk-Jun Yun & Yong-Hwa Kim, 2018. "Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear," Energies, MDPI, vol. 11(5), pages 1-13, May.
    6. Sharon Chiang & Emilian R Vankov & Hsiang J Yeh & Michele Guindani & Marina Vannucci & Zulfi Haneef & John M Stern, 2018. "Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-25, January.
    7. Martin Siegel & Sebastian Coenen & Michael Beltle & Stefan Tenbohlen & Marc Weber & Pascal Fehlmann & Stefan M. Hoek & Ulrich Kempf & Robert Schwarz & Thomas Linn & Jitka Fuhr, 2019. "Calibration Proposal for UHF Partial Discharge Measurements at Power Transformers," Energies, MDPI, vol. 12(16), pages 1-17, August.
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    Cited by:

    1. 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.
    2. 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.

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