Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12619
Title: Erroneous measurement detection in substation automation system using OLS based RBF neural network
Authors: Sheng, S
Duan, X
Chan, WL 
Li, Z
Keywords: Erroneous measurement
Orthogonal least square learning algorithm
Radial basis function neural networks
Substation automation systems
Issue Date: 2009
Publisher: Elsevier
Source: International journal of electrical power and energy systems, 2009, v. 31, no. 7-8, p. 351-355 How to cite?
Journal: International journal of electrical power and energy systems 
Abstract: With the development of communication and information technology over the past decades, Electronic Instrumental Transducer (EIT) and broadband communication network have been prevalent within Substation Automation System (SAS) and power utilities. Since mal-function of EIT and broadband communication network within SAS can produce dangerous erroneous measurements, the risk for the protection system to receive these erroneous measurements and thereafter to mis-operate increase. Pattern identification can be utilized to detect erroneous measurements. In order to achieve satisfying pattern identification precision within time limit imposed by protection systems, Radial Basis Function Neural Network (RBFNN) are investigated in the paper. Orthogonal Least Square (OLS) learning algorithm is used to prune network scale in order to mitigate contradictory requirements of high precision and low time delay. Simulation results show OLS based RBFNN can achieve satisfying performance within limited time.
URI: http://hdl.handle.net/10397/12619
ISSN: 0142-0615
DOI: 10.1016/j.ijepes.2009.03.008
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

12
Last Week
0
Last month
1
Citations as of Sep 22, 2017

WEB OF SCIENCETM
Citations

7
Last Week
0
Last month
0
Citations as of Sep 22, 2017

Page view(s)

37
Last Week
0
Last month
Checked on Sep 25, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.