Improving the diagnosis of incipient faults in power transformers using dissolved gas analysis and multi layer perceptron

S. Nkosi, P. Bokoro

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

1 Citation (Scopus)

Abstract

In this work, a ten year gas production records, obtained from Rodgers ratio codes for gas concentration of H2, CH4, C2H6, C2H4 and C2H2, in a 795 MVA 20/400 kV rated power transformer are used in MLP for improving the diagnosis accuracy of incipient faults (partial discharge, thermal faults, low and high energy discharges, etc...). Numerical modelling of DGA-sourced data was achieved using MATLAB platform. Performance validation for the neural network was assessed in terms of the regression fit (R), the MSE and the MAPE. Results show 2.51 % as optimal value of MAPE which indicate a reasonable accuracy of ANN technique in the prediction of incipient faults.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages112-117
Number of pages6
ISBN (Electronic)9781728136660
DOIs
Publication statusPublished - Jun 2019
Event28th IEEE International Symposium on Industrial Electronics, ISIE 2019 - Vancouver, Canada
Duration: 12 Jun 201914 Jun 2019

Publication series

NameIEEE International Symposium on Industrial Electronics
Volume2019-June

Conference

Conference28th IEEE International Symposium on Industrial Electronics, ISIE 2019
Country/TerritoryCanada
CityVancouver
Period12/06/1914/06/19

Keywords

  • component
  • formatting
  • insert
  • style
  • styling

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering

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