• DocumentCode
    45318
  • Title

    Monitoring Wind Farms With Performance Curves

  • Author

    Kusiak, Andrew ; Verma, Anoop

  • Author_Institution
    Intell. Syst. Lab., Univ. of Iowa, Iowa City, IA, USA
  • Volume
    4
  • Issue
    1
  • fYear
    2013
  • fDate
    Jan. 2013
  • Firstpage
    192
  • Lastpage
    199
  • Abstract
    Three different operational curves-the power curve, rotor curve, and blade pitch curve-are presented for monitoring a wind farm´s performance. A five-year historical data set has been assembled for constructing the reference curves of wind power, rotor speed, and blade pitch angle, with wind speed as an input variable. A multivariate outlier detection approach based on k-means clustering and Mahalanobis distance is applied to this data to produce a data set for modeling turbines. Kurtosis and skewness of bivariate data are used as metrics to assess the performance of the wind turbines. Performance monitoring of wind turbines is accomplished with the Hotelling T2 control chart.
  • Keywords
    control charts; pattern clustering; wind power plants; wind turbines; Hotelling T2 control chart; Mahalanobis distance; bivariate data kurtosis; blade pitch angle; blade pitch curve; k-means clustering; multivariate outlier detection approach; operational curves; performance curves; power curve; rotor curve; rotor speed; wind farm monitoring; wind power; wind turbine modelling; Blades; Data mining; Monitoring; Rotors; Wind farms; Wind speed; Wind turbines; $k$-means clustering; Control chart; Mahalanobis distance; performance monitoring; turbine performance curves;
  • fLanguage
    English
  • Journal_Title
    Sustainable Energy, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1949-3029
  • Type

    jour

  • DOI
    10.1109/TSTE.2012.2212470
  • Filename
    6307908