A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system

Author

Changaroon, Boonserm ; Srivastava, Suresh Chandra ; Thukaram, Dhadbanjan

Author_Institution

Div. of Electr. Eng., Electr. Generating Authority of Thailand, Thailand

Volume

15

Issue

1

fYear

2000

fDate

3/1/2000 12:00:00 AM

Firstpage

103

Lastpage

109

Abstract

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS

Keywords

computer based training; neurocontrollers; oscillations; power system stability; EGAT system; Electricity Generating Authority of Thailand; damping characteristics enhancement; functional link network model; interarea oscillation modes; neural network; neuro-controller; neuro-identifier; on-line training; power system stabilizer; recursive on-line training algorithm; Artificial neural networks; Computer aided software engineering; Damping; Frequency; Neural networks; Power generation; Power system dynamics; Power system modeling; Power system simulation; Power systems;

fLanguage

English

Journal_Title

Energy Conversion, IEEE Transactions on

Publisher

ieee

ISSN

0885-8969

Type

jour

DOI

10.1109/60.849124

Filename

849124