Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing

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. (2018) Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing. IEEE Transactions on Dielectrics and Electrical Insulation, 25 (2). pp. 494-506. ISSN 1070-9878 (https://doi.org/10.1109/TDEI.2018.006766)

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Abstract

Accurate diagnosis of power transformers is critical for the reliable and cost-effective operation of the power grid. Presently there are a range of methods and analytical models for transformer fault diagnosis based on dissolved gas analysis. However, these methods give conflicting results and they are not able to generate uncertainty information associated with the diagnostics outcome. In this situation it is not always clear which model is the most accurate. This paper presents a novel multiclass probabilistic diagnosis framework for dissolved gas analysis based on Bayesian networks and hypothesis testing. Bayesian network models embed expert knowledge, learn patterns from data and infer the uncertainty associated with the diagnostics outcome, and hypothesis testing aids in the data selection process. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset and is shown to have a maximum diagnosis accuracy of 88.9%.

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:Article
    ID code:62741
    Dates:
    Date
    Event
    19 April 2018
    Published
    7 January 2018
    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:08 Jan 2018 14:23
    Last modified:19 Nov 2024 03:32
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/62741
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