Open AccessMultilevel SVM and AI based Transformer Fault Diagnosis using the DGA Data
Author(s) -
Gadepalli Srirama Sarma
Publication year - 2021
Publication title -
journal of informatics electrical and electronics engineering (jieee)
Language(s) - English
Resource type - Journals
ISSN - 2582-7006
DOI - 10.54060/jieee/002.03.001
Subject(s) - dissolved gas analysis , support vector machine , gaussian function , pattern recognition (psychology) , transformer , artificial intelligence , classifier (uml) , gaussian , computer science , data mining , machine learning , engineering , transformer oil , voltage , electrical engineering , physics , quantum mechanics
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.