An application of S VM, RBF and MLP with ARD on bushings

Sizwe M. Dhlamini, Tshilidzi Marwala

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

5 Citations (Scopus)

Abstract

This paper examines classification models using three classes of artificial neural networks (ANN). The first ANN uses Support Vector Machine activation functions. The second uses Multiple -layered Perceptron (MLP) activation functions with automatic relevance detection (ARD). And the third uses Radial Basis activation functions (RBF). In this work the decision is taken to remove or leave a bushing in service based on analysis of bushing parameters using RBF, SVM and MLP, The work finds that the RBF converges to a solution faster than both SVM and MLP. The MLP is the best tool of the three for analyzing large amounts of non-parametric non-linear data. MLP is the most accurate of the three networks. ARD reveals that Methane was the most common cause for action on bushings tested using DGA during the two years evaluation period.

Original languageEnglish
Title of host publication2004 IEEE Conference on Cybernetics and Intelligent Systems
Pages1253-1258
Number of pages6
Publication statusPublished - 2004
Externally publishedYes
Event2004 IEEE Conference on Cybernetics and Intelligent Systems - , Singapore
Duration: 1 Dec 20043 Dec 2004

Publication series

Name2004 IEEE Conference on Cybernetics and Intelligent Systems

Conference

Conference2004 IEEE Conference on Cybernetics and Intelligent Systems
Country/TerritorySingapore
Period1/12/043/12/04

Keywords

  • Bushing
  • Diagnosis
  • Dissolved gas analysis
  • Multiple layered perceptron
  • Radial basis
  • Support vector machines

ASJC Scopus subject areas

  • General Engineering

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