• 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