Title :
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
fDate :
3/1/2000 12:00:00 AM
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;
Journal_Title :
Energy Conversion, IEEE Transactions on
