Title :
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
fDate :
12/1/1999 12:00:00 AM
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;
Journal_Title :
Energy Conversion, IEEE Transactions on
