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ANN and wavelet-based discrimination technique between discharge currents in transformer mineral oils

Published online by Cambridge University Press:  09 May 2012

F. Aberkane ,
H. Moulai ,
A. Nacer ,
F. Benyahia  and
A. Beroual
F. Aberkane*
Affiliation:
Laboratory of Electrical and Industrial Systems, FEI, USTHB, BP 32 Bab Ezzouar, Algiers 16311, Algeria
H. Moulai
Affiliation:
Laboratory of Electrical and Industrial Systems, FEI, USTHB, BP 32 Bab Ezzouar, Algiers 16311, Algeria
A. Nacer
Affiliation:
Laboratory of Electrical and Industrial Systems, FEI, USTHB, BP 32 Bab Ezzouar, Algiers 16311, Algeria
F. Benyahia
Affiliation:
Laboratory of Electrical and Industrial Systems, FEI, USTHB, BP 32 Bab Ezzouar, Algiers 16311, Algeria
A. Beroual
Affiliation:
École Centrale de Lyon, AMPERE Laboratory, UMR CNRS 5005, 36 avenue Guy de Collongue, 69134 Écully, France
*
ae-mail: Abderrahmane.Beroual@eea.ec-lyon.fr
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Abstract

This paper is aimed at the analysis of positive pre-breakdown currents triggered in mineral transformer oil submitted to 50 Hz alternating overvoltages. Different shapes of streamer currents and electrical discharges have been recorded to develop a discrimination technique based on an Artificial Neural Network (ANN) and Wavelet analysis of these currents. This enables us to address a complementary diagnosis tool that can serve as an online transformer monitoring and protection.

Information

Type
Research Article
Information
The European Physical Journal - Applied Physics , Volume 58 , Issue 2 , May 2012 , 20801
Copyright
© EDP Sciences, 2012

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References

Beroual, A. et al., IEEE Electr. Insul. Mag. 14, 6 (1998)CrossRef
Beroual, A., Moulai, H., Arch. Electr. Eng. L, 115 (2001)
Arakelian, V.G., IEEE Electr. Insul. Mag. 18, 26 (2002)CrossRef
Wang, M., Vandermaar, A.J., Srivastava, K.D., IEEE Electr. Insul. Mag. 18, 12 (2002)CrossRef
IEC Standard 60599, “Mineral Oil-Impregnated Electrical Equipment in Service – Guide to the Interpretation of Dissolved and Free Gases Analysis”, 2007
IEEE Std. C57.104, “Guide for interpretation of gases generated in oil-immersed transformer”, (2008), DOI: 10.1109/IEEESTD.2009.4776518
Duval, M., Dukarm, J., IEEE Electr. Insul. Mag. 21, 21 (2005)CrossRef
Rogers, R., IEEE Trans. Dielectr. Electr. Insul. E1–13, 349 (1978)CrossRef
Döernenburg, E., Strittmatter, W., Brown Boveri Rev. 61, 238 (1974)
Duval, M., IEEE Electr. Insul. Mag. 24, 22 (2008)CrossRef
Beroual, A., Tobazeon, R., IEEE Trans. Elect. Insul. EI-21, 613 (1986)CrossRef
Nakao, Y., Itoh, H., Hoshino, S., Sakai, Y., Tagashira, H., IEEE Trans. Dielectr. Electr. Insul. 1, 383 (1994)CrossRef
Torshin, Y.V., IEEE Trans. Dielectr. Electr. Insul. 2, 167 (1995)CrossRef
Lundgaard, L., Linhjel, D., Berg, G., Sigmond, S., IEEE Trans. Dielectr. Electr. Insul. 5, 388 (1998)CrossRef
Lin, C.T., Lee, C.S.G., Neural Fuzzy Systems (Prentice Hall, NJ, USA, 1995), p. 205Google Scholar