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

Tomasz Boczar, Sebastian Borucki, Daniel Jancarczyk, Marcin Bernas and Pawel Kurtasz
Additional contact information
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

Energies, 2022, vol. 15, issue 14, 1-13

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.

Keywords: partial discharges; acoustic emission method; machine learning methods; identification; recognition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/14/5013/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/14/5013/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:14:p:5013-:d:858904

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().