Partial discharge localisation on power transformers using neural networks combined with sectional winding transfer functions as knowledge base

authored by

Peter Werle, Asghar Akbari Azirani, H. Borsi, Ernst Gockenbach

Abstract

In this contribution the applicability of different neural classifiers trained by sectional winding transfer functions (SWTFs) of transformer coils for evaluating wideband electrical measured partial discharges (PD) is presented and discussed. Based on PD measurements on a distribution transformer in the laboratory it is demonstrated, that the introduced technique enables beside a determination of the apparent charge an adequate localisation of the PD origin. The performance and the reproducibility of this method leads to a short outlook on planned investigations and the possibilities for integrating this technique in online condition monitoring systems.

Organisation(s)

High Voltage Engineering and Asset Management Section (Schering Institute)

External Organisation(s)

K.N. Toosi University of Technology (KNTU)

Type

Conference contribution

Pages

579-582

No. of pages

4

Publication date

2001

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

General Engineering, General Materials Science

Electronic version(s)

https://doi.org/10.1109/ISEIM.2001.973734 (Access: Closed)