TITLE:
Support Vector Machine-Based Fault Diagnosis of Power Transformer Using k Nearest-Neighbor Imputed DGA Dataset
AUTHORS:
Zahriah Binti Sahri, Rubiyah Binti Yusof
KEYWORDS:
Missing Values, Dissolved Gas Analysis, Support Vector Machine, k-Nearest Neighbors
JOURNAL NAME:
Journal of Computer and Communications,
Vol.2 No.9,
July
11,
2014
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.