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

页数：6

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