• DocumentCode
    3376715
  • Title

    Power system stabilizer based on artificial neural network

  • Author

    Kumar, Jagdish ; Kumar, P. Pavan ; Mahesh, Aeidapu ; Shrivastava, Ankit

  • Author_Institution
    Dept. of Electr. Eng., PEC Univ. of Technol., Chandigarh, India
  • fYear
    2011
  • fDate
    22-24 Dec. 2011
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    This paper describes a systematic approach for designing a self-tuning adaptive power system stabilizer (PSS) based on artificial neural network (ANN). An ANN is used for self-tuning the parameters of PSS e.g. stabilizing gain Kstab and time constant (T1) for Lead PSS in realtime. The inputs to the ANN are generator terminal active power (P) and reactive power (Q). Investigations are carried out to assess the dynamic performance of the system with self-tuning PSS based on ANN (ST-ANNPSS) over a wide range of loading conditions. The simulations are performed using Matlab/Simulink´s neural network toolbox. The simulation and experimental results demonstrate the effective dynamic performance of the proposed system.
  • Keywords
    neural nets; power engineering computing; power system stability; reactive power; Matlab-Simulink neural network toolbox; ST-ANNPSS; artificial neural network; generator terminal active power; lead PSS; reactive power; self-tuning PSS; self-tuning adaptive power system stabilizer; stabilizing gain; systematic approach; time constant; Artificial neural networks; Generators; Neurons; Oscillators; Power system stability; Rotors; Torque; Artificial Neural Network (ANN); Kstab and T1; power system stabilizer;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Systems (ICPS), 2011 International Conference on
  • Conference_Location
    Chennai
  • Print_ISBN
    INAVLID ISBN
  • Type

    conf

  • DOI
    10.1109/ICPES.2011.6156656
  • Filename
    6156656