Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing

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

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)
197 Downloads (Pure)


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%.
Original languageEnglish
Pages (from-to)494-506
Number of pages12
JournalIEEE Transactions on Dielectrics and Electrical Insulation
Issue number2
Publication statusPublished - 19 Apr 2018


  • dissolved gas analysis
  • transformer diagnosis
  • condition monitoring
  • Bayesian networks
  • normality test
  • probabilistic diagnostics


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