Research on fault signals recognition in GIS based on wavelet theory

Author

Wang Xiaozhe ; Wang Jinping ; Dai Huaizhi ; Li Yuexian

Author_Institution

Coll. of Inf. Sci. & Eng, Northeastern Univ., Shenyang, China

fYear

2012

fDate

23-25 May 2012

Firstpage

1442

Lastpage

1445

Abstract

According to the characteristics of partial discharge (PD) signals of gas insulated switchgear (GIS), a fault type recognition method based on wavelet packet transform is put forward. First the wavelet packet decomposition tree of PD signals is constructed. Then in feature extraction of PD signals, different data fusion algorithms are used in feature vector dimension reduction to enhance sensitivity of feature vector to PD fault signals characteristics. A fault recognizer based on back propagation neural network (BPNN) is designed. The results of simulation show that the wavelet transform based GIS fault signal recognition method is effective.

Keywords

backpropagation; fault diagnosis; feature extraction; gas insulated switchgear; neural nets; partial discharges; signal reconstruction; trees (mathematics); wavelet transforms; GIS fault signal recognition method; backpropagation neural network; fault type recognition method; feature extraction; feature vector dimension reduction; gas insulated switchgear; partial discharge signals; wavelet packet decomposition tree; wavelet packet transform; wavelet theory; Circuit faults; Feature extraction; Gas insulation; Partial discharges; Wavelet packets; Back Propagation Neural Network (BPNN); Electric Power System; Gas Insulated Switchgear (GIS); Partial Discharge (PD); Wavelet Theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2012 24th Chinese

Conference_Location

Taiyuan

Print_ISBN

978-1-4577-2073-4

Type

conf

DOI

10.1109/CCDC.2012.6244230

Filename

6244230