Partial discharge recognition using a neural network

Author

Yamazaki, A. ; Tsutsumi, Y. ; Yonekura, T.

Author_Institution

Ibaraki Univ., Japan

Volume

2

fYear

1994

fDate

3-8 Jul 1994

Firstpage

642

Abstract

A new analyzing technique for partial discharges using a neural network and a Whitehead partial discharge model is proposed. The teacher´s data for the neural network are discharge inception phase data calculated from characteristic values of the discharge, such as discharge inception voltage, statistical time lag or residual voltage after discharge. This method is applied to a typical void discharge with or without UV illumination and their characteristic values are calculated from the measured discharge inception phase data

Keywords

charge measurement; electrical engineering computing; fault diagnosis; image recognition; insulation testing; neural nets; partial discharges; UV illumination; Whitehead partial discharge model; discharge inception phase data; discharge inception voltage; insulation diagnosis; neural network; partial discharge recognition; residual voltage; statistical time lag; void discharge; Algorithm design and analysis; Neural networks; Neurons; Partial discharge measurement; Partial discharges; Pattern analysis; Phase measurement; Pulse measurements; Virtual manufacturing; Voltage;

fLanguage

English

Publisher

ieee

Conference_Titel

Properties and Applications of Dielectric Materials, 1994., Proceedings of the 4th International Conference on

Conference_Location

Brisbane, Qld.

Print_ISBN

0-7803-1307-0

Type

conf

DOI

10.1109/ICPADM.1994.414092

Filename

414092