Bushing fault detection and diagnosis using extension neural network

Christina B. Vilakazi, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

This paper proposes a Extension Neural Network (ENN) based bushing fault detection and diagnosis. Experimentation is done using Dissolve gas-in-oil analysis (DGA) data from bushings based on IEEEc57.104, IEC599 and IEEE production rates methods for oil impregnated paper (OIP) bushings. The optimal learning rate for ENN is selected using Genetic Algorithm (GA). The classification process is a two stage phase. The first stage is the detection which identifies if the bushing is faulty or normal while the second stage determines the nature of fault. A classification rate of 100% and an average of 99.89% is obtained for the detection and diagnosis stage, respectively. It takes 1.98s and 2.02s to train the ENN for the detection and diagnosis stage, respectively.

Original languageEnglish
Title of host publicationINES 2006
Subtitle of host publication10th International Conference on Intelligent Engineering Systems 2006
Pages170-174
Number of pages5
Publication statusPublished - 2006
Externally publishedYes
EventINES 2006: 10th International Conference on Intelligent Engineering Systems 2006 - London, United Kingdom
Duration: 26 Jun 200628 Jun 2006

Publication series

NameINES 2006: 10th International Conference on Intelligent Engineering Systems 2006

Conference

ConferenceINES 2006: 10th International Conference on Intelligent Engineering Systems 2006
Country/TerritoryUnited Kingdom
CityLondon
Period26/06/0628/06/06

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Applied Mathematics
  • Theoretical Computer Science

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