Classification of fault and stray gassing in transformer oil using SVM, NB and KNN algorithms

Al-Katheri, Hussein Hasan and Mohd Yousof, Mohd Fairouz and Illias, Hazlee Azil and Talib, Mohd Aizam (2021) Classification of fault and stray gassing in transformer oil using SVM, NB and KNN algorithms. In: 13th IEEE International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2021, 12 - 14 July 2021, Virtual, Online.

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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.

Item Type: Conference or Workshop Item (Paper)
Funders: Universiti Tun Hussein Onn Malaysia [Grant No: H840]
Uncontrolled Keywords: DGA; Duval triangle; machine learning algorithm; transformer oil
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 16 Oct 2023 09:54
Last Modified: 16 Oct 2023 09:54

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