Eur. Phys. J. Appl. Phys. (2012) 58: 20801
https://doi.org/10.1051/epjap/2012120050

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

¹ Laboratory of Electrical and Industrial Systems, FEI, USTHB, BP 32 Bab Ezzouar, Algiers 16311, Algeria
² École Centrale de Lyon, AMPERE Laboratory, UMR CNRS 5005, 36 avenue Guy de Collongue, 69134 Écully, France

^a e-mail: Abderrahmane.Beroual@eea.ec-lyon.fr

Received: 8 February 2012
Revised: 29 March 2012
Accepted: 4 April 2012
Published online: 9 May 2012

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.