Classification of fault and stray gassing in transformer oil using SVM, NB and KNN algorithms
Document Type
Conference Item
Publication Date
7-1-2021
Abstract
Power transformer is one of the most crucial components in the power system network. A major fault on the transformer can disrupt the power supply, thus causing substantial losses. The dissolved gas analysis (DGA) is used to detect incipient fault based on the transformer oil. However, stray gassing of oil could give false indication to the result. This paper aims to develop a model for considering the results obtained from DGA to investigate transformer oil fault condition. Machine learning (ML) algorithms which are Naïve Bayes (NB), support vector machine (SVM) and K-nearest neighbour (KNN) are trained to classify the DGA data into three categories; not determined (N/D), fault, and stray gassing. The algorithms achieved an accuracy of 93.0, 95.4 and 97.7 respectively. Overall, the algorithms' performance was tested and verified using various user-input data, where correct classification was achieved successfully. © 2021 IEEE.
Keywords
DGA, Duval triangle, machine learning algorithm, transformer oil
Divisions
sch_ecs
Funders
Universiti Tun Hussein Onn Malaysia [Grant No: H840]
Publication Title
Proceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials
Volume
2021-J
Event Title
13th IEEE International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2021
Event Location
Virtual, Online
Event Dates
12 - 14 July 2021
Event Type
conference