IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3167-d802748.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3167/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/9/3167/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Vo-Nguyen Tuyet-Doan & Tien-Tung Nguyen & Minh-Tuan Nguyen & Jong-Ho Lee & Yong-Hwa Kim, 2020. "Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear," Energies, MDPI, vol. 13(8), pages 1-16, April.
    2. Shaorui Qin & Siyuan Zhou & Taiyun Zhu & Shenglong Zhu & Jianlin Li & Zheran Zheng & Shuo Qin & Cheng Pan & Ju Tang, 2021. "Sinusoidal Noise Removal in PD Measurement Based on Synchrosqueezing Transform and Singular Spectrum Analysis," Energies, MDPI, vol. 14(23), pages 1-22, November.
    3. Stefan Tenbohlen & Chandra Prakash Beura & Wojciech Sikorski & Ricardo Albarracín Sánchez & Bruno Albuquerque de Castro & Michael Beltle & Pascal Fehlmann & Martin Judd & Falk Werner & Martin Siegel, 2023. "Frequency Range of UHF PD Measurements in Power Transformers," Energies, MDPI, vol. 16(3), pages 1-21, January.
    4. Krzysztof Walczak, 2023. "Localization of HV Insulation Defects Using a System of Associated Capacitive Sensors," Energies, MDPI, vol. 16(5), pages 1-15, February.
    5. Łukasz Nagi & Michał Kozioł & Jarosław Zygarlicki, 2020. "Optical Radiation from an Electric Arc at Different Frequencies," Energies, MDPI, vol. 13(7), pages 1-9, April.
    6. Marek Florkowski & Dariusz Krześniak & Maciej Kuniewski & Paweł Zydroń, 2020. "Partial Discharge Imaging Correlated with Phase-Resolved Patterns in Non-Uniform Electric Fields with Various Dielectric Barrier Materials," Energies, MDPI, vol. 13(11), pages 1-15, May.
    7. Issouf Fofana & Yazid Hadjadj, 2018. "Power Transformer Diagnostics, Monitoring and Design Features," Energies, MDPI, vol. 11(12), pages 1-5, November.
    8. Janvier Sylvestre N’cho & Issouf Fofana, 2020. "Review of Fiber Optic Diagnostic Techniques for Power Transformers," Energies, MDPI, vol. 13(7), pages 1-24, April.
    9. Franciszek Witos & Aneta Olszewska & Zbigniew Opilski & Agnieszka Lisowska-Lis & Grzegorz Szerszeń, 2020. "Application of Acoustic Emission and Thermal Imaging to Test Oil Power Transformers," Energies, MDPI, vol. 13(22), pages 1-20, November.
    10. Jiaying Deng & Wenhai Zhang & Xiaomei Yang, 2019. "Recognition and Classification of Incipient Cable Failures Based on Variational Mode Decomposition and a Convolutional Neural Network," Energies, MDPI, vol. 12(10), pages 1-16, May.
    11. Alexandru Pîrjan & George Căruțașu & Dana-Mihaela Petroșanu, 2018. "Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain," Energies, MDPI, vol. 11(10), pages 1-42, October.
    12. Sanuri Ishak & Chong Tak Yaw & Siaw Paw Koh & Sieh Kiong Tiong & Chai Phing Chen & Talal Yusaf, 2021. "Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine," Energies, MDPI, vol. 14(19), pages 1-21, October.
    13. Franciszek Witos & Aneta Olszewska, 2023. "Investigation of Partial Discharges within Power Oil Transformers by Acoustic Emission," Energies, MDPI, vol. 16(9), pages 1-20, April.
    14. Seokho Moon & Hansam Cho & Eunji Koh & Yong Sung Cho & Hyoung Lok Oh & Younghoon Kim & Seoung Bum Kim, 2022. "Remanufacturing Decision-Making for Gas Insulated Switchgear with Remaining Useful Life Prediction," Sustainability, MDPI, vol. 14(19), pages 1-13, September.
    15. Krzysztof Walczak & Wojciech Sikorski, 2021. "Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System," Energies, MDPI, vol. 14(18), pages 1-20, September.
    16. Dmitry A. Ivanov & Marat F. Sadykov & Danil A. Yaroslavsky & Aleksandr V. Golenishchev-Kutuzov & Tatyana G. Galieva, 2021. "Non-Contact Methods for High-Voltage Insulation Equipment Diagnosis during Operation," Energies, MDPI, vol. 14(18), pages 1-16, September.
    17. Wojciech Sikorski, 2018. "Active Dielectric Window: A New Concept of Combined Acoustic Emission and Electromagnetic Partial Discharge Detector for Power Transformers," Energies, MDPI, vol. 12(1), pages 1-27, December.
    18. Sara Mantach & Ahmed Ashraf & Hamed Janani & Behzad Kordi, 2021. "A Convolutional Neural Network-Based Model for Multi-Source and Single-Source Partial Discharge Pattern Classification Using Only Single-Source Training Set," Energies, MDPI, vol. 14(5), pages 1-16, March.
    19. 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.
    20. Wang, Bo & Jia, Xiaoyu & Yang, Jian & Wang, Qiuwang, 2022. "Numerical study on temperature rise and structure optimization for a three-phase gas insulated switchgear busbar chamber," Energy, Elsevier, vol. 254(PC).

    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:15:y:2022:i:9:p:3167-:d:802748. 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.