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