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

Zahriah Binti Sahri, Rubiyah Binti Yusof

Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia.
Malaysia Japan International Institute of Technology, Universiti Tekonologi Malaysia, Kuala Lumpur, Malaysia.

DOI: 10.4236/jcc.2014.29004                

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.

Keywords

Missing Values, Dissolved Gas Analysis, Support Vector Machine, k-Nearest Neighbors

Conflicts of Interest

The authors declare no conflicts of interest.

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