Monitoring Wind Farms With Performance Curves

被引:120
作者
Kusiak, Andrew [1 ]
Verma, Anoop [1 ]
机构
[1] Univ Iowa, Intelligent Syst Lab, Iowa City, IA 52242 USA
关键词
Control chart; kappa-means clustering; Mahalanobis distance; performance monitoring; turbine performance curves; MULTIVARIATE SKEWNESS; DIAGNOSIS; KURTOSIS;
D O I
10.1109/TSTE.2012.2212470
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 kappa-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 T-2 control chart.
引用
收藏
页码:192 / 199
页数:8
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