Publication Type : Journal Article
Publisher : JUSST
Source : Journal of University of Shanghai for Science and Technology (JUSST), Volume 23, Issue 5, p.737-744 (2021)
Campus : Bengaluru
School : School of Engineering
Department : Electrical and Electronics
Year : 2021
Abstract : Power transformer is an important link in power system. Utilities will face a huge loss if a fault occurs transformer. The outage can cause loss to industry sector. Transformer incipient fault can be predicted using Dissolved Gas Analysis (DGA) based on gas ratios. The current work is an effort to use SVM to predict transformer incipient fault more precisely. DGA data of various transformer oil samples were collected and analyzed to select the best SVM kernel function and kernel factor to be used and to observe the prediction accuracy.
Cite this Research Publication : A. Kumar and Vidya H. A., “Transformer Incipient fault prediction using Support Vector Machine (SVM) ”, Journal of University of Shanghai for Science and Technology (JUSST), vol. 23, no. 5, pp. 737-744, 2021.