TY - JOUR
T1 - Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing
AU - Aizpurua, Jose Ignacio
AU - Catterson, Victoria M.
AU - Stewart, Brian G.
AU - McArthur, Stephen D. J.
AU - Lambert, Brandon
AU - Ampofo, Bismark
AU - Pereira, Gavin
AU - Cross, James G.
N1 - © 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.
PY - 2018/4/19
Y1 - 2018/4/19
N2 - 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%.
AB - 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%.
KW - dissolved gas analysis
KW - transformer diagnosis
KW - condition monitoring
KW - Bayesian networks
KW - normality test
KW - probabilistic diagnostics
UR - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=94
U2 - 10.1109/TDEI.2018.006766
DO - 10.1109/TDEI.2018.006766
M3 - Article
VL - 25
SP - 494
EP - 506
JO - IEEE Transactions on Dielectrics and Electrical Insulation
JF - IEEE Transactions on Dielectrics and Electrical Insulation
SN - 1070-9878
IS - 2
ER -