Neural network based control for synchronous generators

Author

Swidenbank, E. ; McLoone, S. ; Flynn, D. ; Irwin, GW ; Brown, MD ; Hogg, BW

Author_Institution

Dept. of Electr. & Electron. Eng., Queen´´s Univ., Belfast, UK

Volume

14

Issue

4

fYear

1999

fDate

12/1/1999 12:00:00 AM

Firstpage

1673

Lastpage

1678

Abstract

In this paper, a radial basis function neural network based AVR is proposed. A control strategy which generates local linear models from a global neural model on-line is used to derive controller feedback gains based on the generalised minimum variance technique. Testing is carried out on a micromachine system which enables evaluation of practical implementation of the scheme. Constraints imposed by gathering training data, computational load, and memory requirements for the training algorithm are addressed

Keywords

machine control; neurocontrollers; radial basis function networks; synchronous generators; voltage control; voltage regulators; computational load; controller feedback gains; generalised minimum variance technique; local linear models; memory requirements; micromachine system; neural network based control; on-line global neural model; radial basis function neural network based AVR; synchronous generators; training algorithm; training data; Artificial intelligence; Control systems; Function approximation; Linear feedback control systems; Neural networks; Neurons; Parameter estimation; Polynomials; Synchronous generators; Turbogenerators;

fLanguage

English

Journal_Title

Energy Conversion, IEEE Transactions on

Publisher

ieee

ISSN

0885-8969

Type

jour

DOI

10.1109/60.815122

Filename

815122