• DocumentCode
    1947358
  • Title

    A Messy Genetic Algorithm Based Optimization Scheme for SVC Placement of Power Systems under Critical Operation Contingence

  • Author

    Huang, J.S. ; Negnevitsky, Michael

  • Author_Institution
    Sch. of Comput. & Math, Univ. of Western Sydney, Sydney, NSW
  • Volume
    1
  • fYear
    2008
  • fDate
    12-14 Dec. 2008
  • Firstpage
    467
  • Lastpage
    472
  • Abstract
    In the paper the authors present a messy genetic-algorithm-based optimization scheme for voltage stability enhancement of power systems under critical operation conditions. The placement of SVCs in a power system has been posed as a multi-objective optimization in terms of maximum worst-case reactive margin, highest load voltages at the critical operating points, minimum real power losses and lowest device costs. During the genetic algorithm search for the optimal solution, the most critical disturbance scenario is estimated with the configuration of the original power system and each candidate SVC placement. By using this estimation, the SVC placement can be greatly simplified. With a fuzzy performance index, the multi-objective optimization can be further transformed into a constrained problem with a single non-differentiable objective function containing both continuous and discrete variables.
  • Keywords
    fuzzy set theory; genetic algorithms; power system stability; static VAr compensators; fuzzy performance index; messy genetic algorithm based optimization scheme; multiobjective optimization; power losses; power system stability; static VAR compensators; voltage stability enhancement; Cost function; Genetic algorithms; Power system planning; Power system security; Power system simulation; Power system stability; Power systems; Reactive power; Static VAr compensators; Voltage; messy genetic algorithm; multi-objective optimization; non-linear programming; voltage stability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Software Engineering, 2008 International Conference on
  • Conference_Location
    Wuhan, Hubei
  • Print_ISBN
    978-0-7695-3336-0
  • Type

    conf

  • DOI
    10.1109/CSSE.2008.1148
  • Filename
    4721788