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

Author

Tian, Y. ; Lewin, P.L. ; Davies, A.E. ; Sutton, S.J. ; Swingler, S.G.

Author_Institution

High Voltage Lab., Southampton Univ., UK

fYear

2002

fDate

7-10 Apr 2002

Firstpage

119

Lastpage

123

Abstract

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

Keywords

Fourier transforms; acoustic emission testing; backpropagation; insulation testing; neural nets; organic insulating materials; partial discharge measurement; polymers; power cable insulation; power cable testing; acoustic emission measurements; acoustic emission techniques; artificial neural networks; backpropagation; high voltage cables; partial discharge classification; pattern identification; polymeric insulation defects; signal analysis; Acoustic emission; Acoustic measurements; Acoustic signal detection; Artificial neural networks; Partial discharge measurement; Partial discharges; Pollution measurement; Signal analysis; Signal processing; Voltage;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Insulation, 2002. Conference Record of the 2002 IEEE International Symposium on

Conference_Location

Boston, MA

Print_ISBN

0-7803-7337-5

Type

conf

DOI

10.1109/ELINSL.2002.995895

Filename

995895