Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7844
Title: ANN controlled battery energy storage system for enhancing power system stability
Authors: Tsang, MW
Sutanto, D
Keywords: Adaptive control
Battery storage plants
Control system analysis
Control system synthesis
Learning (artificial intelligence)
Neurocontrollers
Power system control
Power system stability
Issue Date: 2000
Publisher: IET
Source: 2000 International Conference on Advances in Power System Control, Operation and Management, 2000 : APSCOM-00, 30 October-1 November 2000, v. 2, p. 327-331 How to cite?
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.
URI: http://hdl.handle.net/10397/7844
ISBN: 0-85296-791-8
DOI: 10.1049/cp:20000416
Appears in Collections:Conference Paper

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