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Using Clustering Methods for the Identification of Acoustic Emission Signals Generated by the Selected Form of Partial Discharge in Oil-Paper Insulation

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
207--215
Opis fizyczny
Bibliogr. 20 poz., rys., tab., wykr.
Twórcy
autor
  • Institute of Electrical Power and Renewable Energy, Opole University of Technology, Prószkowska 76, 45-758 Opole, Poland, s.borucki@po.opole.pl
autor
  • Institute of Electrical Power and Renewable Energy, Opole University of Technology, Prószkowska 76, 45-758 Opole, Poland
autor
  • Institute of Electrical Power and Renewable Energy, Opole University of Technology, Prószkowska 76, 45-758 Opole, Poland
Bibliografia
  • 1. Akbari A., Setayeshmehr A., Borsi H., Gockenbach E. (2010), Intelligent agent-based system using dissolved gas analysis to detect incipient faults in power transformers, IEEE Electrical Insulation Magazine, 26, 6, 27-40.
  • 2. Basak A. (1999), Condition monitoring of power transformers, Engineering Science and Education Journal, 8, 1, 41-46.
  • 3. Boczar T. (2001), Identification of a specific type of PD form acoustics emission frequency spectra, IEEE Transactions on Dielectrics and Electrical Insulation, 8, 4, 598-606.
  • 4. Boczar T., Cichoń A., Borucki S. (2014), Diagnostic expert system of transformer insulation systems using the acoustic emission method, IEEE Transactions on Dielectrics and Electrical Insulation, 21, 2, 854-865.
  • 5. Borucki S. (2009), Vibroacoustic measurements in a transient state of transformer operatrion, Acta Physica Polonica A, 116, 3, 277-280.
  • 6. Borucki S., Boczar T., Cichoń A., Lorenc M. (2007), The evaluation of neural networks application for recognizing single-source PD forms generated in paper-oil insulation systems based on the AE signal analysis, European Physical Journal Special Topics, 154, 1, 23-29.
  • 7. Borucki S., Cichoń A. (2010), The influence of power transformer load on vibroacoustic signal analysis results [in Polish: Wpływ zmiany obciążenia transformatora energetycznego na wyniki analizy sygnałów wibroakustycznych], Przegląd Elektrotechniczny, 86, 7, 45-47.
  • 8. Cichosz P. (2000), Learning systems [in Polish: Systemy uczące się], WNT, Warszawa.
  • 9. Fuhr J. (2005), Procedure for identification and localization of dangerous partial discharges sources in power transformers, IEEE Transactions on Dielectrics and Electrical Insulation, 12, 5, 1005-1014.
  • 10. Kaźmierski M., Olech W. (2013), Technical diagnostics and monitoring of transformers [in Polish], Printing house of ZPBE Energopomiar-elektryka Sp. z o.o., Gliwice.
  • 11. Krzyśko M., Wołyński W., Górecki T., Skorzybut M. (2008), Learning systems. Pattern recognition, cluster analysis and dimensionality reduction [in Polish: Systemy uczące się. Rozpoznawanie wzorców, analiza skupień i redukcja wymiarowości], WNT, Warszawa.
  • 12. Lalitha E. M., Satish L. (2002), Wavelet analysis for classification of multi-source PD patterns, IEEE Transactions on Dielectrics and Electrical Insulation, 7, 1, 40-47.
  • 13. Majchrzak H. (2017), Problems related to balancing peak power on the example of the Polish National Power System, Archives of Electrical Engineering, 66, 1, 207-221.
  • 14. Olszewska A., Witos F. (2012), Location of partial discharge sources and analysis of signals in chosen power oil transformers by means of acoustic emission method, Acta Physica Polonica A, 122, 5, 921-926.
  • 15. Rodrigo A., Llovera P., Fuster V., Quijano A. (2011), Influence of high frequency current transformers bandwidth on charge evaluation in partial discharge measurements, IEEE Transactions on Dielectrics and Electrical Insulation, 18, 5, 1798-1802.
  • 16. Rubio-Serrano J., Rojas-Moreno M. V., Posada J., Martiĕnez-Tarifa J. M., Robles G., Garcia-Souto J. A. (2012), Electro-acoustic detection, identification and location of PD sources in oil-paper insulation systems, IEEE Transactions on Dielectrics and Electrical Insulation, 19, 5, 1569-1578.
  • 17. Singh J., Sood Y. R., Jarial R. K. (2008), Condition monitoring of power transformers – bibliography survey, IEEE Electrical Insulation Magazin, 24, 3, 11-25.
  • 18. Soltani A., Haghjoo F., Shahrtash S. M. (2012), Compensation of the effects of electrical sensors in measuring PD signals, IET Science, Measurement & Technology, 6, 6, 494-501.
  • 19. Yadav R., Kumar S., Venkatasami A., Lobo A. M., Wagle A. M. (2008), Condition based maintenance of power transformer: a cause study, Proceedings of 2008 International Conference on Condition Monitoring and Diagnosis, CMD, Art. E2-2, pp. 502-504.
  • 20. Zalewski A., Cegieła R. (1999), Matlab – numerical calculations and their applications [in Polish: Matlab – obliczenia numeryczne i ich zastosowania], Nakom, Poznań.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-057fc995-04ad-4f9b-b2c3-39eed150c2a1
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