Artificial neural network and rough set for HV bushings condition monitoring

L. J. Mpanza, T. Marwala

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

7 Citations (Scopus)

Abstract

Most transformer failures are attributed to bushings failures. Hence it is necessary to monitor the condition of bushings. In this paper three methods are developed to monitor the condition of oil filled bushing. Multi-layer perceptron (MLP), Radial basis function (RBF) and Rough Set (RS) models are developed and combined through majority voting to form a committee. The MLP performs better that the RBF and the RS is terms of classification accuracy. The RBF is the fasted to train. The committee performs better than the individual models. The diversity of models is measured to evaluate their similarity when used in the committee.

Original languageEnglish
Title of host publicationINES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings
Pages109-113
Number of pages5
DOIs
Publication statusPublished - 2011
Event15th International Conference on Intelligent Engineering Systems, INES 2011 - Poprad, Slovakia
Duration: 23 Jun 201125 Jun 2011

Publication series

NameINES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings

Conference

Conference15th International Conference on Intelligent Engineering Systems, INES 2011
Country/TerritorySlovakia
CityPoprad
Period23/06/1125/06/11

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

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