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

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 Neu...

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Bibliographic Details
Published inEuropean physical journal. Applied physics Vol. 58; no. 2; pp. 20801 - 20806
Main Authors Aberkane, F., Moulai, H., Nacer, A., Benyahia, F., Beroual, A.
Format Journal Article
LanguageEnglish
Published Les Ulis EDP Sciences 01.05.2012
Subjects
Electric power
Engineering Sciences
Online transformers
Prebreakdown
Electric discharges
Discharge currents
Oil filled transformers
Wavelet analysis
Different shapes
Over-voltages
Electrical discharges
Neural networks
Transformer protection
Mineral oils
Diagnosis tools
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Summary: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.
Bibliography:istex:C9BFCE2FE6AB75FC60EE207AB5C48561CE891C0C
publisher-ID:ap120050
ark:/67375/80W-XVTX69SN-F
PII:S1286004212300505
ISSN:1286-0042
1286-0050
DOI:10.1051/epjap/2012120050