DSpace@nitr >
National Institue of Technology- Rourkela >
Journal Articles >

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/782

Title: Fault Classification and Ground detection using Support Vector Machine
Authors: Samantaray, S R
Dash, P K
Panda, G
Keywords: power transmission faults
power transmission lines
relay protection
support vector machines
Issue Date: 2006
Publisher: IEEE
Citation: IEEE Region 10 Conference TENCON 2006. 2006 14-17 Nov. 2006 On page(s): 1-3
Abstract: This paper presents a new approach for the fault classification and ground detection in transmission line in large power system networks using support vector machine (SVM). The proposed method uses post fault current and voltage samples for 1/4th cycle (5 samples) from the inception of the fault as inputs to the SVM. SVM-1 is trained with current and voltage samples to provide faulty phase involved and SVM-2 is trained with peak of the ground current to provide the involvement of the ground in the fault process. The SVMs are trained with Gaussian kernel with different parameter values to get the most optimized classifier. The proposed method converges very fast and thus provides fast and accurate protection scheme for distance relaying
Description: Copyright for the paper belongs to IEEE
URI: http://dx.doi.org/10.1109/TENCON.2006.344216
http://hdl.handle.net/2080/782
Appears in Collections:Journal Articles

Files in This Item:

File Description SizeFormat
srs8.pdf3563KbAdobe PDFView/Open

Show full item record

All items in DSpace are protected by copyright, with all rights reserved.

 

Powered by DSpace Feedback