DocumentCode :
2629131
Title :
Artificial intelligence methodology for separation and classification of partial discharge signals
Author :
Contin, A. ; Cavallini, A. ; Montanari, G.C. ; Pasini, G. ; Puletti, F.
Author_Institution :
Trieste Univ., Italy
Volume :
2
fYear :
2000
fDate :
2000
Firstpage :
522
Abstract :
Results of investigations performed in order to improve the current diagnostic techniques used for the evaluation of insulation systems of HV apparatus are presented in this paper. Improvements come from the development of a new measuring system which allows the digital acquisition of Partial Discharge (PD) signals and a new separation method, based on a Fuzzy Classifier, for the analysis of the PD-pulse shape signals. The identification of the classes, relevant to different PD phenomena, is then performed by means of PD-pulse height and phase analysis. The proposed approach is supported by the analysis of PD data obtained from insulation systems of stator bars with artificially-reproduced defects
Keywords :
artificial intelligence; fuzzy set theory; insulation testing; machine insulation; partial discharge measurement; signal classification; stators; HV apparatus; artificial intelligence; digital acquisition; fuzzy classifier; insulation diagnosis; partial discharge measurement; phase analysis; pulse height analysis; signal classification; signal separation; stator bar; Artificial intelligence; Data analysis; Fuzzy systems; Insulation; Partial discharge measurement; Partial discharges; Performance analysis; Performance evaluation; Shape measurement; Signal analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Insulation and Dielectric Phenomena, 2000 Annual Report Conference on
Conference_Location :
Victoria, BC
Print_ISBN :
0-7803-6413-9
Type :
conf
DOI :
10.1109/CEIDP.2000.884013
Filename :
884013
Link To Document :
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