DocumentCode :
3491433
Title :
ANN controlled battery energy storage system for enhancing power system stability
Author :
Tsang, M.W. ; Sutanto, D.
Author_Institution :
Dept. of Electr. Eng., Hong Kong Polytech., Hung Hom, China
Volume :
2
fYear :
2000
fDate :
30 Oct.-1 Nov. 2000
Firstpage :
327
Abstract :
This paper describes an application of an adaptive artificial neural network (ANN) controller to continuously control the charging and discharging of a battery energy storage system (BESS) to improve the stability of an electric power system. The simulation studies have included a detailed model of the generator including its excitation controller and governor, as well as a comprehensive BESS model, including the DC battery model and the switch operation associated with the power converter. An online training artificial neural network controller is continuously trained to directly control the BESS operation to damp power system oscillations in various power system operating conditions. Simulation results show that this ANN-controller can adaptively learn and update its control strategy to improve the system stability under different system operating conditions.
Keywords :
adaptive control; battery storage plants; control system analysis; control system synthesis; learning (artificial intelligence); neurocontrollers; power system control; power system stability; DC battery model; adaptive artificial neural network controller; adaptive learning; battery energy storage system; charging control; control design; control simulation; control strategy; discharging control; excitation controller; governor; online training; power system operating conditions; power system oscillations damping; power system stability enhancement;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Advances in Power System Control, Operation and Management, 2000. APSCOM-00. 2000 International Conference on
Print_ISBN :
0-85296-791-8
Type :
conf
DOI :
10.1049/cp:20000416
Filename :
950363
Link To Document :
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