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

Publication Title

Proceedings of the IEEE International Conference on Properties and Applications of Dielectric Materials

DOI

10.1109/ICPADM49635.2021.9493941

Funders

Universiti Tun Hussein Onn Malaysia [Grant No: H840]

Volume

2021-July

First Page

326

Last Page

329

Publisher

Institute of Electrical and Electronics Engineers Inc.

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

ISBN

978-172818238-4

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