• DocumentCode
    3325191
  • Title

    Experimental studies with continually online trained artificial neural network identifiers for multiple turbogenerators on the electric power grid

  • Author

    Venayagamoorthy, G.K. ; Harley, R.G. ; Wunsch, D.C.

  • Author_Institution
    Lab. of Appl. Comput. Intelligence, Missouri Univ., Rolla, MO, USA
  • Volume
    2
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    1267
  • Abstract
    The increasing complexity of a modern power grid highlights the need for advanced system identification techniques for effective control of power systems. This paper provides a new method for nonlinear identification of turbogenerators in a 3-machine 6-bus power system using online trained feedforward neural networks. Each turbogenerator in the power system is equipped with a neuro-identifier, which is able to identify its particular turbogenerator and the rest of the network to which it is connected from moment to moment, based on only local measurements. Each neuro-identifier can then be used in the design of a nonlinear neurocontroller for each turbogenerator in such a multi-machine power system. Experimental results for the neuro-identifiers are presented to prove the validity of the concept
  • Keywords
    feedforward neural nets; learning (artificial intelligence); neurocontrollers; power system identification; real-time systems; turbogenerators; electric power grid; feedforward neural networks; identification; multiple machine power system; neural network; neurocontroller; online learning; turbogenerators; Artificial neural networks; Control systems; Feedforward neural networks; Neural networks; Power grids; Power system control; Power system measurements; Power systems; System identification; Turbogenerators;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7044-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2001.939543
  • Filename
    939543