NDLI: PD Pattern Recognition Using ANFIS

Content Provider	IEEE Xplore Digital Library
Author	Chalashkanov, N. Kolev, N. Dodd, S. Fothergill, J.C.
Copyright Year	2008
Description	Author affiliation: Tech. Univ. of Sofia, Sofia (Kolev, N.) \|\| Dept. of Eng., Univ. of Leicester, Leicester (Chalashkanov, N.; Dodd, S.; Fothergill, J.C.)
Abstract	An application of an adaptive neuro-fuzzy inference system (ANFIS) has been investigated for partial discharge (PD) pattern recognition. The proposed classifier was used to discriminate between PD patterns occurring in internal voids. Three different void shapes were considered in this work, namely flat, square and narrow. Initially, the input feature vector used for classification was based on 15 statistical parameters. The discrimination capabilities of each feature were assessed by applying discriminant analysis. This analysis suggested that some of the features possess much higher discriminatory power than the others. As a result, a simplified classifier with reduced feature vector has been obtained. The results demonstrate the importance in identifying and removing redundancy in the input feature vector for reliable PD identification.
Starting Page	417
Ending Page	420
File Size	2281661
Page Count	4
File Format	PDF
ISBN	9781424425488
DOI	10.1109/CEIDP.2008.4772865
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern recognition Partial discharges Fault location Testing Signal processing algorithms Voltage Pulse measurements Classification algorithms Dielectrics and electrical insulation Adaptive systems
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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