A robust system stabilizer configuration using artificial neural network based on linear optimal control (student paper competition)

Author

Youssef, M.Z. ; Jain, P.K. ; Mohamed, E.A.

Author_Institution

Dept. of Electr. & Comput. Eng., Queen´´s Univ., Kingston, Ont., Canada

Volume

1

fYear

2003

fDate

4-7 May 2003

Firstpage

569

Abstract

An efficient configuration of an adaptive power system stabilizer (PSS) based on the artificial neural network (ANN) and the linear optimal control (LOC) is presented in this paper. The proposed PSS combines the advantages of conventional stabilizer (CPSS), optimizing LOC strategy and the quick response of ANN. The ANN was trained using the data generated by the optimal control stabilizer (LOC-PSS). Different PSSs are presented for performance comparison. MATLAB simulations prove that the proposed PSS significantly improves the dynamic response of the power system over various loading conditions, and different disturbances.

Keywords

neural nets; optimal control; power system control; power system simulation; power system stability; robust control; MATLAB simulations; adaptive power system stabilizer; artificial neural network; linear optimal control; loading conditions; optimal control stabilizer; optimizing LOC strategy; robust system stabilizer configuration; student paper competition; Adaptive control; Adaptive systems; Artificial neural networks; Lab-on-a-chip; Optimal control; Power system control; Power system dynamics; Power system simulation; Programmable control; Robust control;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 2003. IEEE CCECE 2003. Canadian Conference on

ISSN

0840-7789

Print_ISBN

0-7803-7781-8

Type

conf

DOI

10.1109/CCECE.2003.1226460

Filename

1226460