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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.

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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