• DocumentCode
    2501017
  • Title

    UPFC online PI controller design using particle swarm optimization algorithm and artificial neural networks

  • Author

    Asadi, Mohammad Reza ; Sadr, Vahid Gohari

  • Author_Institution
    Dept. of Electr. Eng., Amirkabir Univ. of Technol., Tehran
  • fYear
    2008
  • fDate
    1-3 Dec. 2008
  • Firstpage
    473
  • Lastpage
    478
  • Abstract
    Sometimes power utilities in contingency conditions should work far away from their pre-designed conditions. Various control strategies have been reported recently to control UPFC in different operating conditions. PI regulators used for controlling UPFC suffer from the inadequacies of providing suitable control and transient stability enhancement over a wide range of system operating conditions. A new adaptive controller using particle swarm optimization (PSO) algorithm and artificial neural networks (ANN) is proposed to improve efficiency of UPFCpsilas PI controller in damping power system oscillations. The effectiveness of the proposed method is demonstrated through computer simulation using a multi-machine power system with a single UPFC.
  • Keywords
    PI control; neural nets; particle swarm optimisation; power system control; PI regulators; UPFC online PI controller design; adaptive controller; artificial neural networks; computer simulation; multi-machine power system; particle swarm optimization algorithm; power system oscillations; transient stability enhancement; Adaptive control; Algorithm design and analysis; Artificial neural networks; Control systems; Particle swarm optimization; Power system simulation; Power system stability; Power system transients; Programmable control; Regulators; Power system stability; UPFC; adaptive controller; inter-area oscillations; neural networks; particle swarm optimization (PSO);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Conference, 2008. PECon 2008. IEEE 2nd International
  • Conference_Location
    Johor Bahru
  • Print_ISBN
    978-1-4244-2404-7
  • Electronic_ISBN
    978-1-4244-2405-4
  • Type

    conf

  • DOI
    10.1109/PECON.2008.4762520
  • Filename
    4762520