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 :
بازگشت