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

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/207

Title: Fault classification and location using HS-transform and radial basis function neural network
Authors: Samantaray, S R
Dash, P K
Panda, G
Keywords: Distance Protection
Energy Change Calculations
HS Transform
RBFNN
Issue Date: 2006
Publisher: Elsevier
Citation: Electric Power Systems Research, (Accepted Postprint)
Abstract: A new approach for protection of transmission lines has been presented in this paper. The proposed technique consists of preprocessing the fault current and voltage signal sample using hyperbolic S-transform (HS-transform) to yield the change in energy and standard deviation at the appropriate window variation. After extracting these two features, a decision of fault or no-fault on any phase or multiple phases of the transmission line is detected, classified, and its distance to the relaying point found out using radial basis function neural network (RBFNN) with recursive least square (RLS) algorithm. The ground detection is done by a proposed indicator ‘index’. As HS-transform is very less sensitive to noise compared to wavelet transform, the proposed method provides very accurate and robust relaying scheme for distance protection.
Description: Copyright for this article belongs to Elsevier Science Ltd http://dx.doi.org/10.1016/j.epsr.2005.11.003
URI: http://hdl.handle.net/2080/207
Appears in Collections:Journal Articles

Files in This Item:

File Description SizeFormat
samantaray.pdf1316KbAdobe PDFView/Open

Show full item record

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

 

Powered by DSpace Feedback