DocumentCode
1681807
Title
Implementation of an adaptive neural network identifier for effective control of turbogenerators
Author
Venayagamoorthy, G.K. ; Harley, R.G.
Author_Institution
Dept. of Electron. Eng., ML Sultan Technikon, Durban, South Africa
fYear
1999
Firstpage
134
Abstract
This paper describes an on-line identification technique for modelling a turbogenerator system. The dynamics of a single turbogenerator infinite bus system are modelled using an adaptive artificial neural network identifier (AANNI) based on continual online training (COT). This paper goes further to show that multilayered perceptrons with deviation signals as inputs and outputs trained using the standard backpropagation algorithm retain past learned information despite COT. Simulation and practical results are presented.
Keywords
backpropagation; identification; machine control; multilayer perceptrons; power engineering computing; turbogenerators; adaptive neural network identifier; backpropagation algorithm; continual online training; deviation signals; dynamics modelling; inputs; multilayered perceptrons; on-line identification technique; outputs; turbogenerator infinite bus system dynamics; turbogenerator system modelling; turbogenerators control; Adaptive control; Adaptive systems; Artificial neural networks; Multilayer perceptrons; Neural networks; Programmable control; Signal processing; Turbogenerators; USA Councils; Voltage;
fLanguage
English
Publisher
ieee
Conference_Titel
Electric Power Engineering, 1999. PowerTech Budapest 99. International Conference on
Conference_Location
Budapest, Hungary
Print_ISBN
0-7803-5836-8
Type
conf
DOI
10.1109/PTC.1999.826565
Filename
826565
Link To Document