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
Link To Document :
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