Partial discharge pattern classification for an oil-pressboard interface

A. Abubakar Mas' ud, BG Stewart, SG McMeekin, A. Nesbitt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper compares the ability of a Single Neural Network (SNN) and an Ensemble Neural Network (ENN) in classifying and discriminating oil-pressboard interface partial discharge (PD) degredation. Discharges were sustained for 15 hours and PD patterns captured, evaluated and correlated with the tracking damage on the pressboard surface. For the same experimental arrangement two samples were stressed, one at 18.5kV (rms) and the other at 30kV (rms).
Original languageEnglish
Title of host publicationProceedings of the 2012 IEEE International Symposium on Electrical Insulation (ISEI)
Subtitle of host publicationConference Record of the 2012 IEEE International Symposium on Electrical Insulation (ISEI 2012)
PublisherIEEE
Pages122-126
Number of pages5
ISBN (Electronic)9781467304863
ISBN (Print)9781467304887
DOIs
Publication statusPublished - 2012

Keywords

  • surface tracking
  • high voltage
  • ensemble neural networks
  • single neural network

Fingerprint Dive into the research topics of 'Partial discharge pattern classification for an oil-pressboard interface'. Together they form a unique fingerprint.

  • Cite this

    Abubakar Mas' ud, A., Stewart, BG., McMeekin, SG., & Nesbitt, A. (2012). Partial discharge pattern classification for an oil-pressboard interface. In Proceedings of the 2012 IEEE International Symposium on Electrical Insulation (ISEI): Conference Record of the 2012 IEEE International Symposium on Electrical Insulation (ISEI 2012) (pp. 122-126). IEEE. https://doi.org/10.1109/ELINSL.2012.6251440