IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i14p5013-d858904.html
   My bibliography  Save this article

Application of Selected Machine Learning Techniques for Identification of Basic Classes of Partial Discharges Occurring in Paper-Oil Insulation Measured by Acoustic Emission Technique

Author

Listed:
  • Tomasz Boczar

    (Institute of Electric Power Engineering and Renewable Energy, Opole University of Technology, 45-758 Opole, Poland)

  • Sebastian Borucki

    (Institute of Electric Power Engineering and Renewable Energy, Opole University of Technology, 45-758 Opole, Poland)

  • Daniel Jancarczyk

    (Department of Computer Science and Automatics, University of Bielsko-Biala, 43-309 Bielsko-Biala, Poland)

  • Marcin Bernas

    (Department of Computer Science and Automatics, University of Bielsko-Biala, 43-309 Bielsko-Biala, Poland)

  • Pawel Kurtasz

    (Walbrzych Special Economic Zone, Invest-Park, 58-306 Walbrzych, Poland)

Abstract

The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine learning methods. As part of the re-search, the identification involved AE signals registered in laboratory conditions for eight basic classes of PDs that occur in paper-oil insulation systems of high-voltage power equipment. On the basis of acoustic signals emitted by PDs and by application of the frequency descriptor that took the form of a signal power density spectrum (PSD), the assessment involved the possibility of identifying individual types of PD by the analyzed classification algorithms. As part of the research, the results obtained with the use of five independent classification mechanisms were analyzed, namely: k-Nearest Neighbors method (kNN), Naive Bayes Classification, Support Vector Machine (SVM), Random Forests and Probabilistic Neural Network (PNN). The best results were achieved using the SVM classification tuned with polynomial core, which obtained 100% accuracy. Similar results were achieved with the kNN classifier. Random Forests and Naïve Bayes obtained high accuracy over 97%. Throughout the study, identification algorithms with the highest effectiveness in identifying specific forms of PD were established.

Suggested Citation

  • Tomasz Boczar & Sebastian Borucki & Daniel Jancarczyk & Marcin Bernas & Pawel Kurtasz, 2022. "Application of Selected Machine Learning Techniques for Identification of Basic Classes of Partial Discharges Occurring in Paper-Oil Insulation Measured by Acoustic Emission Technique," Energies, MDPI, vol. 15(14), pages 1-13, July.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5013-:d:858904
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/14/5013/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/14/5013/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    2. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    3. Sonia Barrios & David Buldain & María Paz Comech & Ian Gilbert & Iñaki Orue, 2019. "Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress," Energies, MDPI, vol. 12(13), pages 1-16, June.
    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. 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.

    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. Juan Carlos Chávez & Felipe J. Fonseca & Manuel Gómez-Zaldívar, 2017. "Resoluciones de disputas comerciales y desempeño económico regional en México. (Commercial Disputes Resolution and Regional Economic Performance in Mexico)," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(1), pages 79-93, May.
    2. Chen, Ray-Bing & Chen, Ying & Härdle, Wolfgang K., 2014. "TVICA—Time varying independent component analysis and its application to financial data," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 95-109.
    3. Yan Yu Chen & Chun-Cheih Chao & Fu-Chen Liu & Po-Chen Hsu & Hsueh-Fen Chen & Shih-Chi Peng & Yung-Jen Chuang & Chung-Yu Lan & Wen-Ping Hsieh & David Shan Hill Wong, 2013. "Dynamic Transcript Profiling of Candida albicans Infection in Zebrafish: A Pathogen-Host Interaction Study," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
    4. Plat, Richard, 2009. "Stochastic portfolio specific mortality and the quantification of mortality basis risk," Insurance: Mathematics and Economics, Elsevier, vol. 45(1), pages 123-132, August.
    5. Kondylis, Athanassios & Whittaker, Joe, 2008. "Spectral preconditioning of Krylov spaces: Combining PLS and PC regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2588-2603, January.
    6. Simplice A. Asongu & Nicholas M. Odhiambo, 2019. "Governance, capital flight and industrialisation in Africa," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-22, December.
    7. M. J. Aziakpono & S. Kleimeier & H. Sander, 2012. "Banking market integration in the SADC countries: evidence from interest rate analyses," Applied Economics, Taylor & Francis Journals, vol. 44(29), pages 3857-3876, October.
    8. Bianca Maria Colosimo & Luca Pagani & Marco Grasso, 2024. "Modeling spatial point processes in video-imaging via Ripley’s K-function: an application to spatter analysis in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 429-447, January.
    9. Ouyang, Yaofu & Li, Peng, 2018. "On the nexus of financial development, economic growth, and energy consumption in China: New perspective from a GMM panel VAR approach," Energy Economics, Elsevier, vol. 71(C), pages 238-252.
    10. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    11. Ionela Munteanu & Adriana Grigorescu & Elena Condrea & Elena Pelinescu, 2020. "Convergent Insights for Sustainable Development and Ethical Cohesion: An Empirical Study on Corporate Governance in Romanian Public Entities," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    12. Daniel Boss & Annick Hoffmann & Benjamin Rappaz & Christian Depeursinge & Pierre J Magistretti & Dimitri Van de Ville & Pierre Marquet, 2012. "Spatially-Resolved Eigenmode Decomposition of Red Blood Cells Membrane Fluctuations Questions the Role of ATP in Flickering," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-10, August.
    13. Doukas, Haris & Papadopoulou, Alexandra & Savvakis, Nikolaos & Tsoutsos, Theocharis & Psarras, John, 2012. "Assessing energy sustainability of rural communities using Principal Component Analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 1949-1957.
    14. Paschalis Arvanitidis & Athina Economou & Christos Kollias, 2016. "Terrorism’s effects on social capital in European countries," Public Choice, Springer, vol. 169(3), pages 231-250, December.
    15. Rizvi, Syed Kumail Abbas & Rahat, Birjees & Naqvi, Bushra & Umar, Muhammad, 2024. "Revolutionizing finance: The synergy of fintech, digital adoption, and innovation," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    16. Teerachai Amnuaylojaroen & Pavinee Chanvichit, 2024. "Historical Analysis of the Effects of Drought on Rice and Maize Yields in Southeast Asia," Resources, MDPI, vol. 13(3), pages 1-18, March.
    17. -, 2015. "The effects of climate change on the coasts of Latin America and the Caribbean: Climate variability, dynamics and trends," Documentos de Proyectos 39866, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    18. Dorota Toczydlowska & Gareth W. Peters & Man Chung Fung & Pavel V. Shevchenko, 2017. "Stochastic Period and Cohort Effect State-Space Mortality Models Incorporating Demographic Factors via Probabilistic Robust Principal Components," Risks, MDPI, vol. 5(3), pages 1-77, July.
    19. Weili Duan & Bin He & Daniel Nover & Guishan Yang & Wen Chen & Huifang Meng & Shan Zou & Chuanming Liu, 2016. "Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods," Sustainability, MDPI, vol. 8(2), pages 1-15, January.
    20. Joanna Jasnos, 2021. "Hydrogeochemical Characteristics of Geothermal Waters from Mesozoic Formations in the Basement of the Central Part of the Carpathian Foredeep and the Carpathians (Poland) Using Multivariate Statistica," Energies, MDPI, vol. 14(13), pages 1-31, July.

    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:14:p:5013-:d:858904. 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.