Improving the accuracy of transformer DGA diagnosis in the presence of conflicting evidence
- Resource Type
- Conference
- Authors
- Aizpurua, Jose Ignacio; Catterson, Victoria M.; Stewart, Brian G.; McArthur, Stephen D. J.; Lambert, Brandon; Ampofo, Bismark; Pereira, Gavin; Cross, James G.
- Source
- 2017 IEEE Electrical Insulation Conference (EIC) Electrical Insulation Conference (EIC), 2017 IEEE. :286-289 Jun, 2017
- Subject
- Aerospace
Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
Power, Energy and Industry Applications
Transportation
Bayes methods
Degradation
Power transformer insulation
Oil insulation
Probabilistic logic
Random variables
DGA
transformer diagnosis
condition monitoring
Bayesian networks
evidence combination
ensembles
- Language
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%.