Identification of the defective equipments in GIS using the self organising map

T Lin, R K Aggarwal, C H Kim

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Condition monitoring for gas insulated switchgear (GIS) requires an accurate and reliable identification of the defective equipment in it for maintenance purposes. In this paper, a feature extraction procedure is explored, which is based on the power spectral density (PSD) of the denoised partial discharges (PDs) emanating from the defective equipment in the GIS. Furthermore, artificial intelligence techniques, in particular, the self organising map (SOM), are investigated for their roles as classifiers to precisely identify this defective equipment, based on the PSD feature patterns. The performance of the SOM-based classifier is ascertained by using the PDs acquired from GIS in the Korean 154-kV EHV transmission networks.
Original languageEnglish
Pages (from-to)644-650
Number of pages7
JournalGeneration, Transmission and Distribution, IEE Proceedings-
Volume151
Issue number5
DOIs
Publication statusPublished - 2004

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Electric switchgear
Self organizing maps
Partial discharges
Power spectral density
Classifiers
Gases
Electric power transmission networks
Condition monitoring
Artificial intelligence
Feature extraction

Cite this

Identification of the defective equipments in GIS using the self organising map. / Lin, T; Aggarwal, R K; Kim, C H.

In: Generation, Transmission and Distribution, IEE Proceedings-, Vol. 151, No. 5, 2004, p. 644-650.

Research output: Contribution to journalArticle

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