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Abstract

The article presents the results concerning the use of clustering methods to identify signals of acoustic emission (AE) generated by partial discharge (PD) in oil-paper insulation. The conducted testing featured qualitative analysis of the following clustering methods: single linkage, complete linkage, average linkage, centroid linkage and Ward linkage. The purpose of the analysis was to search the tested series of AE signal measurements, deriving from three various PD forms, for elements of grouping (clusters), which are most similar to one another and maximally different than in other groups in terms of a specific feature or adopted criteria. Then, the conducted clustering was used as a basis for attempting to assess the effectiveness of identification of particular PD forms that modelled exemplary defects of the power transformer’s oil-paper insulation system. The relevant analyses and simulations were conducted using the Matlab estimation environment and the clustering procedures available in it. The conducted tests featured analyses of the results of the series of measurements of acoustic emissions generated by the basic PD forms, which were obtained in laboratory conditions using spark gap systems that modelled the defects of the power transformer’s oil-paper insulation.
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Authors and Affiliations

Sebastian Borucki
Jacek Łuczak
Dariusz Zmarzły
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Abstract

The paper presents the results of the application of the hierarchical clustering methods for the classification of the acoustic emission (AE) signals generated by eight basic forms of partial discharges (PD), which can occur in paper-oil insulation of power transformers. Based on the registered AE signals from the particular PD forms, using a frequency descriptor in the form of the power spectral density (PSD) of the signal, their representation in the form of the set of points on plane XY was created. Next, these sets were subjected to analysis using research algorithms consisting of selected clustering methods. Based on the suggested numeric performance indicators, the analysis of the degree of reproduction of the actual distribution of points showing the particular time waveforms of the AE signals from eight adopted PD forms (PD classes) in the obtained clusters was carried out. As a result of the analyses carried out, the clustering algorithms of the highest effectiveness in the identification of all eight PD classes, classified simultaneously, where indicated. Within the research carried out, an attempt to draw general conclusions as to the selection of the most effective hierarchical clustering method studied and the similarity function to be used for classification of the selected basic PD forms.
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Bibliography

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Authors and Affiliations

Sebastian Borucki
1
Jacek Łuczak
1
Marcin Lorenc
1

  1. Opole University of Technology, Opole, Poland

Authors and Affiliations

Krzysztof Klimaszewski
Sebastian Borucki
Jacek Łuczak
Dariusz Zmarzły
Michał Kunicki
Andrzej Cichoń
Franciszek Witos
Czesław Leśnik
Dariusz Pleban
Milan Timko
Tomasz Rogala
Piotr Serafin
Tadeusz Gudra
Jerzy Filipiak
Dariusz Banasiak
Krzysztof Herman
Krzysztof Opieliński
Tomasz Hejczyk
Krzysztof Jasek
Mateusz Pasternak
Michał Grabka
Witold Kardyś
Andrzej Milewski
Adam Kawalec
Marta Okoń-Fąfara
Bartłomiej Fąfara
Aneta Olszewska
Piotr Pruchnicki
Marcin Wrzosek
Józef Nicpoń
Przemysław Podgórski
Tadeusz Pustelny
Marcin Szugajew
Olga Stec
Grzegorz Szerszeń
Daria Wotzka
Agnieszka Boruń
Adam Bald
Marzena Dzida
Sylwia Jężak
Monika Geppert-Rybczyńska
Katarzyna Kaczmarek
Tomasz Hornowski
Arkadiusz Józefczak
M. Kubovčíková
A. Skumiel
Z. Rozynek
M. Timko
Peter Kopcansky
Bogumił Linde
Vyacheslav N. Verveyko
Marina V. Verveyko
Darya V. Verveyko
Andrey Yu. Verisokin
Nikita S. Chebrov
Witold Mikulski
Danuta Augustyńska
Bożena Smagowska

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