• DocumentCode
    577008
  • Title

    Intelligent PID controller design of SSSC for power system stability enhancement

  • Author

    Alizadeh, Mojtaba ; Ganjefar, Soheil ; Farahani, Mohsen

  • Author_Institution
    Electr. Eng. Dept., Univ. of Bu-Ali Sina, Hamedan, Iran
  • fYear
    2011
  • fDate
    27-29 Dec. 2011
  • Firstpage
    7
  • Lastpage
    12
  • Abstract
    Most of the controllers for FACTS devices are based on the PI controller. Although the PI controllers are simple and easy to design, their performance deteriorates when the controlled object is highly nonlinear. This paper aims to propose an On-Line Self-Learning PID (OLSL-PID) controller design of SSSC for power system stability enhancement and to overcome the PI controller problems. The PID controller parameters are updated in on-line mode using BP algorithm based on the information provided by the Adaptive Sef-Recurrent Wavelet Neural Network (ASRWNNI) which is a powerful fast-acting identifier because of its local nature, self-recurrent structure and stable training algorithm with Adaptive Learning Rates (ALRs) based on discrete lyapunov stability theorem. The proposed control scheme is applied to multi-machine power system and simulation results are also presented to complement the theoretical discussion.
  • Keywords
    Lyapunov methods; PI control; control system synthesis; discrete systems; intelligent control; learning systems; neurocontrollers; power system stability; three-term control; ALR; ASRWNNI; FACTS devices; OLSL-PID; PI controller; SSSC; adaptive learning rates; adaptive self-recurrent wavelet neural network; discrete lyapunov stability theorem; fast-acting identifier; intelligent PID controller design; multimachine power system; on-line self-learning PID controller design; power system stability enhancement; Control systems; Convergence; Neural networks; Oscillators; Power system dynamics; Power system stability; Power transmission lines; Adaptive Learning Rates; Adaptive PID controller; Flexible AC Transmission System (FACTS); Power system stability; Self-Recurrent Wavelet Neural Network (SRWNN);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control, Instrumentation and Automation (ICCIA), 2011 2nd International Conference on
  • Conference_Location
    Shiraz
  • Print_ISBN
    978-1-4673-1689-7
  • Type

    conf

  • DOI
    10.1109/ICCIAutom.2011.6356622
  • Filename
    6356622