Improving the accuracy of transformer DGA diagnosis in the presence of conflicting evidence

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Aizpurua, Jose Ignacio and Catterson, Victoria M. and Stewart, Brian G. and McArthur, Stephen D. J. and Lambert, Brandon and Ampofo, Bismark and Pereira, Gavin and Cross, James G.; (2017) Improving the accuracy of transformer DGA diagnosis in the presence of conflicting evidence. In: 2017 IEEE Electrical Insulation Conference (EIC). IEEE, USA. ISBN 9781509039654 (https://doi.org/10.1109/EIC.2017.8004698)

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Abstract

Transformers are critical assets for the reliable and cost-effective operation of the power grid. Transformers may fail if condition monitoring does not identify degraded conditions in time. Dissolved Gas Analysis (DGA) focuses on the examination of the dissolved gasses in the transformer oil and there exist different methods for transformer fault diagnosis based on different analyses of the gassing levels. However, these methods can give conflicting results, and it is not always clear which model is most accurate in a given situation. This paper presents a novel evidence combination framework for DGA based on Bayesian networks. Bayesian network models embed expert knowledge along with learned data patterns and evidence combination which aids in the consistency of analysis. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset with a maximum diagnosis accuracy of 88.3%.

ORCID iDs

Aizpurua, Jose Ignacio ORCID logoORCID: https://orcid.org/0000-0002-8653-6011, Catterson, Victoria M. ORCID logoORCID: https://orcid.org/0000-0003-3455-803X, Stewart, Brian G., McArthur, Stephen D. J. ORCID logoORCID: https://orcid.org/0000-0003-1312-8874, Lambert, Brandon, Ampofo, Bismark, Pereira, Gavin and Cross, James G.;
  • Item type:Book Section
    ID code:60889
    Dates:
    Date
    Event
    18 August 2017
    Published
    19 May 2017
    Accepted
    Notes:© 2017 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:09 Jun 2017 11:44
    Last modified:11 Nov 2024 15:10
    URI:https://strathprints.strath.ac.uk/id/eprint/60889
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