• DocumentCode
    991659
  • Title

    Correlation of wind speed between neighboring measuring stations

  • Author

    Bechrakis, Dimitrios A. ; Sparis, Panagiotis D.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Democritus Univ. of Thrace, Xanthi, Greece
  • Volume
    19
  • Issue
    2
  • fYear
    2004
  • fDate
    6/1/2004 12:00:00 AM
  • Firstpage
    400
  • Lastpage
    406
  • Abstract
    A method for establishing wind speed correlation between neighboring measuring stations is presented in this paper. The aim of this study is to develop a model, in which given the wind speed at a particular site to simulate the wind speed at another, nearby site, in order to estimate the wind power of an area. This method takes into account the evolution of the sample cross correlation function (SCCF) of wind speed in time domain and uses an artificial neural network to perform the wind speed simulation. Four separate pairs of wind data measuring stations at two different regions were examined. Tests showed that the higher the SCCF value between two sites, the better simulation achieved. Also, in a pair of stations under investigation the reference station must be the one that contains more information in its wind speed signal, in order to obtain the optimum simulation performance.
  • Keywords
    correlation methods; neural nets; power system simulation; wind power; wind power plants; artificial neural network; neighboring measuring stations; sample cross correlation function; wind data measuring station; wind power; wind speed correlation; Area measurement; Artificial neural networks; Discrete event simulation; Neural networks; Testing; Time measurement; Velocity measurement; Wind energy; Wind forecasting; Wind speed; Correlation; neural network applications; simulation; wind energy;
  • fLanguage
    English
  • Journal_Title
    Energy Conversion, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8969
  • Type

    jour

  • DOI
    10.1109/TEC.2004.827040
  • Filename
    1300707