PD pattern recognition for stator bar models with six kinds of characteristic vectors using BP network

Author

Gao, Kai ; Tan, Kexiong ; Li, Fuqi ; Wu, Chengqi

Author_Institution

Dept. of Electr. Eng., Tsinghua Univ., Beijing, China

Volume

9

Issue

3

fYear

2002

fDate

6/1/2002 12:00:00 AM

Firstpage

381

Lastpage

389

Abstract

The application of six different kinds of characteristic vectors to recognize PD sources is studied. Four kinds of model bars are used to simulate typical partial discharges in generator stator winding. The PD signals were measured by using a computer aided digital sampling system. The sampling results are processed by six kinds of feature extraction methods and different characteristic vectors are obtained. Then these vectors are used as input patterns for BP network. Recognition results using all six kinds of vectors are reasonable. Further analysis shows that vectors formed by moment features or fractal dimensions possess fairly good abilities of pattern identification and data compression

Keywords

backpropagation; data compression; feature extraction; fractals; insulation testing; partial discharges; pattern recognition; stators; backpropagation etwork; characteristic vector; computer aided digital sampling system; data compression; feature extraction; fractal dimension; generator winding; insulation testing; partial discharge; pattern recognition; stator bar model; Application software; Bars; Character recognition; Computational modeling; Feature extraction; Partial discharges; Pattern analysis; Pattern recognition; Signal sampling; Stator windings;

fLanguage

English

Journal_Title

Dielectrics and Electrical Insulation, IEEE Transactions on

Publisher

ieee

ISSN

1070-9878

Type

jour

DOI

10.1109/TDEI.2002.1007700

Filename

1007700