Selecting appropriate machine learning classifiers for DGA diagnosis

Aizpurua Unanue, Jose Ignacio and Catterson, Victoria and Stewart, Brian G and McArthur, Stephen and Lambert, Brandon and Ampofo, Bismark and Pereira, Gavin and Cross, James; (2018) Selecting appropriate machine learning classifiers for DGA diagnosis. In: 2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena. IEEE Dielectrics and and Electrical Insulation Society, Piscataway, N.J., pp. 153-156. ISBN 978-1-5386-1194-4 (https://doi.org/10.1109/CEIDP.2017.8257475)

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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.

ORCID iDs

Aizpurua Unanue, Jose Ignacio ORCID logoORCID: https://orcid.org/0000-0002-8653-6011, Catterson, Victoria, Stewart, Brian G, McArthur, Stephen ORCID logoORCID: https://orcid.org/0000-0003-1312-8874, Lambert, Brandon, Ampofo, Bismark, Pereira, Gavin and Cross, James;
  • Item type:Book Section
    ID code:62273
    Dates:
    Date
    Event
    15 January 2018
    Published
    10 October 2017
    Accepted
    Notes:© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:07 Nov 2017 15:42
    Last modified:18 Nov 2024 01:20
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/62273
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