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Multilevel SVM and AI based Transformer Fault Diagnosis using the DGA Data

GVSSN Srirama Sarma, B. Ravindranath Reddy, Pradeep M. Nirgude, Vasudeva Naidu
Research Paper | Journal Paper
Volume 2 , Issue 3 , PP 1-16
DOI: https://doi.org/10.54060/JIEEE/002.03.001

Abstract

The Dissolved Gas Analysis (DGA) is utilized as a test for the detection of incipient problems in transformers, and condition monitoring of transformers using software-based diagnosis tools has become crucial. This research uses dissolved gas analysis as an intelligent fault classification of a transformer. The Multilayer SVM technique is used to determine the classification of faults and the name of the gas. The learned classifier in the multilayer SVM is trained with the training samples and can classify the state as normal or fault state, which contains six fault categories. In this paper, polynomial and Gaussian functions are utilized to assess the effectiveness of SVM diagnosis. The results demonstrate that the combination ratios and graphical representation technique is more suitable as a gas signature and that the SVM with the Gaussian function outperforms the other kernel functions in diagnosis accuracy.

Key-Words / Index Term

Dissolved gas analysis, multilevel Support vector machine, Kernel Functions, Transformer fault diagnosis, Combination of ratios and graphical representation

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