Uncertainty-aware fusion of probabilistic classifiers for improved transformer diagnostics

Aizpurua, Jose Ignacio and Catterson, Victoria M. and Stewart, Brian G. and McArthur, Stephen D. J. and Lambert, Brandon and Cross, James G. (2021) Uncertainty-aware fusion of probabilistic classifiers for improved transformer diagnostics. IEEE Transactions on Systems, Man and, Cybernetics: Systems, 51 (1). pp. 621-633. ISSN 2168-2216 (https://doi.org/10.1109/TSMC.2018.2880930)

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Abstract

Transformers are critical assets for the reliable operation of the power grid. Transformers may fail in service if monitoring models do not identify degraded conditions in time. Dissolved gas analysis (DGA) focuses on the examination of dissolved gasses in transformer oil to diagnose the state of a transformer. Fusion of black-box (BB) classifiers, also known as an ensemble of diagnostics models, have been used to improve the accuracy of diagnostics models across many fields. When independent classifiers diagnose the same fault, this method can increase the veracity of the diagnostics. However, if these methods give conflicting results, it is not always clear which model is most accurate due to their BB nature. In this context, the use of white-box (WB) models can help resolve conflicted samples effectively by incorporating uncertainty information and improve the classification accuracy. This paper presents an uncertainty-aware fusion method to combine BB and WB diagnostics methods. The effectiveness of the proposed approach is validated using two publicly available DGA datasets.

  • Item type:Article
    ID code:66473
    Dates:
    Date
    Event
    1 January 2021
    Published
    3 December 2018
    Published Online
    5 November 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 2019 11:52
    Last modified:09 Apr 2024 00:27
    URI:https://strathprints.strath.ac.uk/id/eprint/66473
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