Back close

Transformer Incipient fault prediction using Support Vector Machine (SVM)

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)

Url : https://jusst.org/wp-content/uploads/2021/05/Transformer-Incipient-fault-prediction-using-Support-Vector-Machine-SVM-2.pdf

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.

Admissions Apply Now