• DocumentCode
    1778948
  • Title

    Implementation of intelligent AGC in PSAT for optimal use in Smart Grids

  • Author

    Malik, Sarmad M. ; Sun Yingyun ; Khan, A. Zeb

  • Author_Institution
    State Key Lab. of Alternate Electr. Power Syst., North China Electr. Power Univ., Beijing, China
  • fYear
    2014
  • fDate
    2-6 June 2014
  • Firstpage
    186
  • Lastpage
    191
  • Abstract
    With the development of Smart Grids worldwide, efforts have been focused on optimizing the role of Smart Grid in the present electrical system. The integration of renewable energy resources to the grid presents a great challenge since they are intermittent and vary constantly causing frequency fluctuations. Automatic Generation Control (AGC) solves this problem by keeping the frequency close to nominal value and maintaining the balance between generation and demand. AGC has been implemented in various softwares as an added functionality. Power Systems Analysis Toolbox (PSAT) is a MATLAB toolbox designed for power flow computations but it lacks AGC implementation. This paper presents AGC implementation on PSAT using Artificial Neural Networks (ANN) controller and PID controller. The goal is to optimize PSAT by adding extra functionalities to it. The AGC design is implemented on IEEE 14-bus system on PSAT. A comparison of ANN controller and PID controller is also presented. The results show successful addition of AGC to PSAT library. The AGC design helps to control the frequency, has high performance and can be extended to other power systems.
  • Keywords
    frequency control; neurocontrollers; power generation control; power system analysis computing; renewable energy sources; smart power grids; three-term control; IEEE 14-bus system; MATLAB toolbox; PID controller; PSAT; artificial neural networks controller; automatic generation control; frequency control; frequency fluctuations; intelligent AGC; power flow computations; power systems analysis toolbox; renewable energy resources; smart grids; Artificial intelligence; Artificial neural networks; Automatic generation control; Generators; Mathematical model; Power systems; Training; Area Control Error (ACE); Artificial Intelligence (Al); Artificial Neural Network (ANN); Automatic Generation Control (AGC); Power Systems Analysis Toolbox (PSAT); Renewable Energy Resources;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Energy and Power Systems (IEPS), 2014 IEEE International Conference on
  • Conference_Location
    Kyiv
  • Print_ISBN
    978-1-4799-2265-9
  • Type

    conf

  • DOI
    10.1109/IEPS.2014.6874176
  • Filename
    6874176