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


Autoria(s): Changaroon, B; Srivastava, SC; Thukaram, D
Data(s)

01/03/2000

Resumo

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

Formato

application/pdf

Identificador

http://eprints.iisc.ernet.in/44225/1/A_Neural.pdf

Changaroon, B and Srivastava, SC and Thukaram, D (2000) A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system. In: IEEE Transactions on Energy Conversion, 15 (1). pp. 103-109.

Publicador

IEEE

Relação

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=849124

http://eprints.iisc.ernet.in/44225/

Palavras-Chave #Electrical Engineering
Tipo

Journal Article

PeerReviewed