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