• DocumentCode
    23129
  • Title

    Optimization of the Wind Turbine Layout and Transmission System Planning for a Large-Scale Offshore WindFarm by AI Technology

  • Author

    Yuan-Kang Wu ; Ching-Yin Lee ; Chao-Rong Chen ; Kun-Wei Hsu ; Huang-Tien Tseng

  • Author_Institution
    Dept. of Electr. Eng., Nat. Chung Cheng Univ., Chiayi, Taiwan
  • Volume
    50
  • Issue
    3
  • fYear
    2014
  • fDate
    May-June 2014
  • Firstpage
    2071
  • Lastpage
    2080
  • Abstract
    The interest in the utilization of offshore wind power is increasing significantly worldwide. A typical offshore windfarm may have hundreds of generators, which is outspread in the range of several to tens of kilometers. Therefore, there are many feasible schemes for the wind turbine location and internal line connection in a wind farm. The planner must search for an optimal one from these feasible schemes, usually with a maximum wind power output and the lowest installation and operation cost. This paper proposes a novel procedure to determine the optimization wind turbine location and line connection topology by using artificial intelligence techniques: The genetic algorithm is utilized in the optimal layouts for the offshore wind farm, and the ant colony system algorithm is utilized to find the optimal line connection topology. Furthermore, the wake effect, real cable parameters, and wind speed series are also considered in this research. The concepts and methods proposed in this study could help establish more economical and efficient offshore wind farms in the world.
  • Keywords
    ant colony optimisation; artificial intelligence; genetic algorithms; offshore installations; power system interconnection; power transmission planning; wind power plants; wind turbines; AI technology; ant colony system algorithm; genetic algorithm; internal line connection; large-scale offshore windfarm; line connection topology; maximum wind power output; offshore wind power; transmission system planning; wake effect; wind turbine layout; wind turbine location; Equations; Genetic algorithms; Mathematical model; Optimization; Rotors; Wind farms; Wind turbines; Ant Colony System; Ant colony system (ACS); Artificial Intelligent; Genetic Algorithm; Offshore Wind Power; Optimization; Wake effect; artificial intelligence; genetic algorithm (GA); offshore wind power; optimization; wake effect;
  • fLanguage
    English
  • Journal_Title
    Industry Applications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0093-9994
  • Type

    jour

  • DOI
    10.1109/TIA.2013.2283219
  • Filename
    6607158