DocumentCode :
2353499
Title :
Classification of partial discharge signals by means of auto-correlation function evaluation
Author :
Contin, A. ; Pastore, S.
Author_Institution :
Trieste Univ.
fYear :
2006
fDate :
11-14 June 2006
Firstpage :
302
Lastpage :
303
Abstract :
A new algorithm for the separation of partial discharge (PD) signals due to multiple sources is presented in this paper. It evaluates the similarity of the signals shape by comparing their auto-correlation functions (ACFs), on the assumption that the same PD source can exhibit signals having similar ACFs. The new classification algorithm is based on a modified K-mean clustering (KMC) method. A laboratory test of the proposed algorithm is reported. The proposed classification method may constitute a step forward in the automatic signal separation
Keywords :
correlation methods; partial discharge measurement; pattern clustering; signal classification; signal sources; source separation; statistical analysis; K-mean clustering algorithm; PD source; auto-correlation function; automatic signal separation; partial discharge measurement; signal classification; Autocorrelation; Classification algorithms; Clustering algorithms; Laboratories; Noise shaping; Partial discharges; Pulse measurements; Shape; Signal processing; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Insulation, 2006. Conference Record of the 2006 IEEE International Symposium on
Conference_Location :
Toronto, Ont.
ISSN :
1089-084X
Print_ISBN :
1-4244-0333-2
Type :
conf
DOI :
10.1109/ELINSL.2006.1665317
Filename :
1665317
Link To Document :
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