Novel Method for the Identification of Defective Anemometers in Wind Farms

A novel method for the identification of defective anemometers is shown in this paper. The identification of defective wind data is very important, because the actual power curve of a wind turbine is conditioned by the quality of its anemometer. This means that the low energy production presented by a turbine with a deformed actual power curve with respect to the ideal power curve of fabrication may be due to technical problems of the turbine like pitch misalignment or due to a defective anemometer. Here we present a novel multidimensional estimation method applied for the first time to anemometer's data based on Kernel Probability Density Function which improves the classical unidimensional methods such as the Kolmogorov-Smirnov test. This method is applied in a bi-dimensional way for the zonal and meridional components of wind and allows identifying the Best in Class anemometer (BIC) of the wind farm and obtaining a first identification of the defective anemometers. After that, other visual verification technique have been used versus the referential BIC anemometer (Taylor Diagram) and coherent results have been obtained.