• DocumentCode
    2676751
  • Title

    Self-tuning nodal voltage regulation in power systems based on optimal pole-shifting technique

  • Author

    Fusco, Giuseppe ; Russo, Mario

  • Author_Institution
    Universita degli Studi di Cassino
  • fYear
    0
  • fDate
    0-0 0
  • Abstract
    The design of a indirect self-tuning power system nodal voltage regulator using the pole-shifting technique is presented. A recursive least-squares (RLS) algorithm with variable forgetting factor and dead-zone estimates at each sampling step the parameters of an approximated discrete-time linear model describing the system dynamics from the regulation node. Based on the estimated parameters, the regulator polynomials are obtained as the solution of the Diophantine equation which imposes, according to the pole-shifting approach, closed-loop pole locations ensuring the minimization of the normalized quadratic control input. The presence of limits imposed on the control input is also taken into account. Simulations results are finally report to verify the regulator performance
  • Keywords
    least squares approximations; parameter estimation; poles and towers; polynomials; power system control; tuning; voltage control; Diophantine equation; approximated discrete-time linear model; closed-loop pole locations; dead-zone estimates; normalized quadratic control input minimization; optimal pole-shifting technique; parameters estimation; recursive least-squares algorithm; regulator polynomials; self-tuning nodal voltage regulation; variable forgetting factor; Linear approximation; Parameter estimation; Power system dynamics; Power system modeling; Power systems; Recursive estimation; Regulators; Resonance light scattering; Sampling methods; Voltage control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Engineering Society General Meeting, 2006. IEEE
  • Conference_Location
    Montreal, Que.
  • Print_ISBN
    1-4244-0493-2
  • Type

    conf

  • DOI
    10.1109/PES.2006.1709160
  • Filename
    1709160