Online learning recurrent neural network stabilization controller for multi-machine power system

Author

Senjyu, Tomonobu ; Yoshiteru, Morishima ; Uezato, Katsumi

Author_Institution

Ryukyus Univ., Okinawa, Japan

Volume

1

fYear

2000

fDate

2000

Firstpage

223

Abstract

This paper presents an online learning recurrent neural network stabilization controller to improve the transient stability of a power system. Since the proposed recurrent neural network is of the online tuning type, it can stabilize the power system for different system parameters, operating conditions, and fault point. The proposed controller robustness and effectiveness in damping power system oscillations are illustrated through simulations

Keywords

damping; genetic algorithms; learning (artificial intelligence); neurocontrollers; oscillations; power system control; power system transient stability; recurrent neural nets; controller robustness; fault point; genetic algorithm; online learning recurrent neural network; operating conditions; power system oscillations damping; recurrent neural network stabilization controller; transient stability; Control systems; Power system control; Power system faults; Power system simulation; Power system stability; Power system transients; Power systems; Recurrent neural networks; Synchronous generators; Tuning;

fLanguage

English

Publisher

ieee

Conference_Titel

Power System Technology, 2000. Proceedings. PowerCon 2000. International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-6338-8

Type

conf

DOI

10.1109/ICPST.2000.900060

Filename

900060