Uncertainty-aware fusion of probabilistic classifiers for improved transformer diagnostics

Jose Ignacio Aizpurua, Victoria M. Catterson, Brian G. Stewart, Stephen D. J. McArthur, Brandon Lambert, James G. Cross

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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 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 black-box nature. In this context, the use of white-box 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 black-box and white-box diagnostics methods. The effectiveness of the proposed approach is validated using two publicly available DGA datasets.
Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Systems Man and Cybernetics: Systems
Early online date3 Dec 2018
Publication statusE-pub ahead of print - 3 Dec 2018



  • condition monitoring
  • transformer diagnostics
  • ensembles
  • classifiers
  • uncertainty

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