• DocumentCode
    2943658
  • Title

    Adaptive Genetic Algorithm Applied in Coordinated Control of PSS and STATCOM

  • Author

    Liu Zhijian ; Yu Jilai ; Shu Hongchun

  • Author_Institution
    Dept. of Electr. Eng., Harbin Inst. of Technol., Harbin, China
  • Volume
    3
  • fYear
    2009
  • fDate
    11-12 April 2009
  • Firstpage
    171
  • Lastpage
    174
  • Abstract
    Firstly, an adaptive genetic algorithm (AGA) is proposed. For traditional genetic algorithm, it easily premature and falls into local optima. A better initial population should have differences among the individuals and distributes uniformly in the whole solution space. In AGA of this paper, initial population is selected according to the Hamming distances to solve this question. Adaptive crossing and mutation probability method also is presented to optimize the population and find the global solution. Secondly, a multi-aim optimal designing method which is based on AGA is applied in the cooperation control of power system stabilizer (PSS) and static compensator (STATCOM). Simulation results show that, used the solution presented in the paper, the stability of power angle and voltage can be effectively enhanced.
  • Keywords
    genetic algorithms; power system stability; probability; static VAr compensators; Hamming distances; STATCOM; adaptive crossing method; adaptive genetic algorithm; coordinated control; mutation probability method; power angle stability; power system stabilizer; simulation result; static compensator; voltage stability; Adaptive control; Automatic voltage control; Design methodology; Genetic algorithms; Genetic mutations; Optimization methods; Power system control; Power system simulation; Power system stability; Programmable control; PSS; Power system stability; STATCOM; adaptive genetic algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Measuring Technology and Mechatronics Automation, 2009. ICMTMA '09. International Conference on
  • Conference_Location
    Zhangjiajie, Hunan
  • Print_ISBN
    978-0-7695-3583-8
  • Type

    conf

  • DOI
    10.1109/ICMTMA.2009.401
  • Filename
    5203174