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

被引:2
|
作者
Aberkane, F. [1 ]
Moulai, H. [1 ]
Nacer, A. [1 ]
Benyahia, F. [1 ]
Beroual, A. [2 ]
机构
[1] USTHB, FEI, Lab Elect & Ind Syst, Algiers 16311, Algeria
[2] Ecole Cent Lyon, CNRS, UMR 5005, AMPERE Lab, F-69134 Ecully, France
来源
EUROPEAN PHYSICAL JOURNAL-APPLIED PHYSICS | 2012年 / 58卷 / 02期
关键词
PREBREAKDOWN PHENOMENA; PROPAGATION; STREAMERS;
D O I
10.1051/epjap/2012120050
中图分类号
O59 [应用物理学];
学科分类号
摘要
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.
引用
收藏
页数:6
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