Application of acoustic emission techniques and artificial neural networks to partial discharge classification

被引：12

作者：

Tian, Y ^{[1
]}

Lewin, PL ^{[1
]}

Davies, AE ^{[1
]}

Sutton, SJ ^{[1
]}

Swingler, SG ^{[1
]}

机构：

[1] Univ Southampton, High Voltage Lab, Southampton SO17 1EJ, Hants, England

来源：

CONFERENCE RECORD OF THE 2002 IEEE INTERNATIONAL SYMPOSIUM ON ELECTRICAL INSULATION | 2002年

关键词：

D O I：

10.1109/ELINSL.2002.995895

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Partial discharge (PD) detection, signal analysis and pattern identification, using acoustic emission measurements and the back-propagation (BP) artificial neural network (ANN) have been investigated. The measured signals were processed using three-dimensional patterns and short duration Fourier transforms (SDFT). Investigation indicates that using BP ANN with the SDFT components for classifying different PD patterns provides very good overall results.

引用

页码：119 / 123

页数：5