Selecting appropriate machine learning classifiers for DGA diagnosis

Jose Ignacio Aizpurua Unanue, Victoria Catterson, Brian G Stewart, Stephen McArthur, Brandon Lambert, Bismark Ampofo, Gavin Pereira, James Cross

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

1 Citation (Scopus)
209 Downloads (Pure)

Abstract

Dissolved gas analysis (DGA) is a common method of assessing transformer health. There are a number of machine learning classifiers reported to give a high accuracy on specific datasets, such as Artificial Neural Networks or Support Vector Machines. When these methods reach the same conclusion about the type of fault present, this can give an increased confidence in the veracity of the diagnosis. However, it is critical to analyze and quantify the strength of these classifiers in the presence of conflicting data to test their practicality for usage in the field. This paper investigates the adequacy of different machine
learning based DGA diagnosis models in the presence of conflicting data. The proposed method will aid engineers with the selection of machine learning models so as to maximize the usability and accuracy in the presence of conflicting data.
Original languageEnglish
Title of host publication2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena
Place of PublicationPiscataway, N.J.
Pages153-156
Number of pages4
DOIs
Publication statusPublished - 15 Jan 2018

Keywords

  • dissolved gas analysis
  • transformer health assessment
  • transformers

Fingerprint Dive into the research topics of 'Selecting appropriate machine learning classifiers for DGA diagnosis'. Together they form a unique fingerprint.

Cite this