Support Vector Machine Based Fault Diagnosis Of Power Transformer Using k Nearest Neighbor Imputed DGA Dataset

Zahriah, Sahri and Rubiyah, Yusof (2014) Support Vector Machine Based Fault Diagnosis Of Power Transformer Using k Nearest Neighbor Imputed DGA Dataset. Journal Of Computer And Communications, 2. pp. 22-31. ISSN 2327-5219

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Abstract

Missing values are prevalent in real-world datasets and they may reduce predictive performance of a learning algorithm. Dissolved Gas Analysis (DGA), one of the most deployable methods for detecting and predicting incipient faults in power transformers is one of the casualties. Thus, this paper proposes filling-in the missing values found in a DGA dataset using the k-nearest neighbor imputation method with two different distance metrics: Euclidean and Cityblock. Thereafter, using these imputed datasets as inputs, this study applies Support Vector Machine (SVM) to built models which are used to classify transformer faults. Experimental results are provided to show the effectiveness of the proposed approach.

Item Type:	Article
Uncontrolled Keywords:	Missing Values, Dissolved Gas Analysis, Support Vector Machine, k-Nearest Neighbors
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User:	Mohd Hannif Jamaludin
Date Deposited:	16 Aug 2016 02:57
Last Modified:	07 Sep 2021 03:15
URI:	http://eprints.utem.edu.my/id/eprint/16936
Statistic Details:	View Download Statistic

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