DocumentCode
3466018
Title
Hopfield network and parallel genetic algorithm for solving state estimate in power systems
Author
Khoa, T.Q.D. ; Binh, P.T.T. ; Khoa, T.V.
Author_Institution
Fac. of Electr. & Electron. Eng., Ho Chi Minh City Univ. of Technol., Vietnam
Volume
1
fYear
2004
fDate
21-24 Nov. 2004
Firstpage
845
Abstract
In power systems, the state estimation computation takes an important role in security controls and the weighted least squares (WLS) method has been widely used at present This paper presents the artificial neural network for static state estimation. Hopfield neural network (HNN) and parallel genetic algorithms (PGA) are employed to solve static state estimation on the 5 bus system.
Keywords
Hopfield neural nets; genetic algorithms; least squares approximations; power engineering computing; power system state estimation; 5 bus system; Hopfield neural network; artificial neural network; parallel genetic algorithm; power system state estimation; security controls; static state estimation; weighted least squares method; Artificial neural networks; Computer networks; Control systems; Genetic algorithms; Hopfield neural networks; Least squares approximation; Power system security; Power systems; State estimation; Weight control;
fLanguage
English
Publisher
ieee
Conference_Titel
Power System Technology, 2004. PowerCon 2004. 2004 International Conference on
Print_ISBN
0-7803-8610-8
Type
conf
DOI
10.1109/ICPST.2004.1460111
Filename
1460111
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