DocumentCode :
437409
Title :
Parameter tuning of the conventional power system stabilizer by artificial neural network
Author :
Chusanapiputt, S. ; Withiromprasert, K. ; Chitnumsab, P. ; Phoomvurhisarn, S.
Author_Institution :
Dept. of Electr. Power Eng., Mahanakom Univ., Bangkok, Thailand
Volume :
1
fYear :
2004
fDate :
21-24 Nov. 2004
Firstpage :
554
Abstract :
This paper presents parameter tuning of conventional power system stabilizer (CPSS) by artificial neural network (ANN). The ANN in the paper is radial basis function network (RBFN), whose parameters are chosen by adaptive orthogonal least squares (adaptive OLS) algorithm, to compensate error of linear model of power system where a fixed-parameter CPSS is analyzed. The adaptive OLS algorithm is developed from the orthogonal least squares (OLS) algorithm to reduce the neural network size more efficiently. When the system condition is changed, this makes the fixed-parameter CPSS less efficient than a varied-parameter CPSS by ANN. Moreover, the adjustment of damping coefficient using the gradient descent method improves the oscillation damping.
Keywords :
gradient methods; least squares approximations; neurocontrollers; oscillations; power system stability; radial basis function networks; tuning; adaptive orthogonal least squares algorithm; artificial neural network; conventional power system stabilizer; gradient descent method; oscillation damping; parameter tuning; radial basis function network; Algorithm design and analysis; Artificial neural networks; Damping; Least squares methods; Neural networks; Power system analysis computing; Power system modeling; Power systems; Radial basis function networks; Tuning;
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.2004.1460056
Filename :
1460056
Link To Document :
بازگشت