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Dong-Hui, L., Jian-Peng, B. and Xiao-Yun, S. (2008) The Study of Fault Diagnosis Model of DGA for Oil-Immersed Transformer Based on Fuzzy Means Kernel Clustering and SVM Multi-Class Object Simplified Structure. Proceedings of the International Conference on Machine Learning and Cybernetics, 12-15 July 2008, 1505-1509.

has been cited by the following article:

• 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.