Online source recognition of partial discharge for gas insulated substations using independent component analysis

Author

Chang, C.S. ; Jin, J. ; Chang, C. ; Hoshino, T. ; Hanai, M. ; Kobayashi, N.

Author_Institution

Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore

Volume

13

Issue

4

fYear

2006

Firstpage

892

Lastpage

902

Abstract

To perform reliable insulation diagnosis for gas-insulated substation (GIS), detectable partial discharge (PD) should be identified quickly and effectively. With the increasing application of high voltage DC transmission, PD identification in such systems becomes more and more important. Therefore, a novel technique based on the analysis of ultra high frequency (UHF) resonance waveforms is proposed in this paper to meet the requirement. With the help of independent component analysis, the most dominating features are identified directly from UHF resonance signals without phase information. Using the identified features as the input, a neural network is implemented for recognizing sources of PD in SF₆ and separating from the corona in air within a very short time

Keywords

SF₆ insulation; UHF measurement; condition monitoring; corona; feature extraction; gas insulated substations; independent component analysis; partial discharge measurement; signal detection; source separation; SF₆; condition monitoring; corona; gas insulated substation; high voltage DC transmission; independent component analysis; insulation diagnosis; neural network; online source recognition; partial discharge detection; ultra high frequency resonance waveform; Gas insulation; Geographic Information Systems; Independent component analysis; Neural networks; Partial discharges; Resonance; Resonant frequency; Signal processing; Substations; Voltage;

fLanguage

English

Journal_Title

Dielectrics and Electrical Insulation, IEEE Transactions on

Publisher

ieee

ISSN

1070-9878

Type

jour

DOI

10.1109/TDEI.2006.1667751

Filename

1667751