Abstract
A novel despiking method is presented for in-stationary wind lidar velocity measurements. A finite difference approach yields the upper and lower bounds for a valid velocity reading. The sole input to the algorithm is the velocity series and optionally a far- field reference to the temporal variation in the velocity. The new algorithm is benchmarked against common despiking algorithms using a dataset acquired by three synchronised lidars in the upstream area of a full-scale wind turbine rotor and an artificially created space-time series with controlled spike contamination. By accounting for variations in space and time, this approach yields improvements in spike detection for in-stationary lidar measurements of about 25% over other more established stationary methods. Furthermore it proofs to be robust even for large numbers of spikes.
Highlights
Lidars have been developing into important measurement instruments for the wind industry over the last decade, as they have proven to give accurate measurements and are more versatile than any other classic wind measurement system
All algorithms detect the obvious spike in figure 6 at t = 1444 s, but both the interquartile range (IQ) and the phase space (PS) methods remove a vast amount of valid data
The novel finite difference based despiking algorithm accounts for the in-stationary nature of scanning lidar velocity measurements
Summary
Lidars have been developing into important measurement instruments for the wind industry over the last decade, as they have proven to give accurate measurements and are more versatile than any other classic wind measurement system. Scanning lidars, like the short-range WindScanners [1], continuously change their focus location to measure velocities over an entire two-dimensional plane, thereby increasing the probability of spikes penetrating into the velocity signal. In the related field of acoustic Doppler velocimetry it has been shown that the 3D phase space method, originally proposed by Goring and Nikora [2] and later modified by Wahl [3], is highly efficient [4] for turbulent flow data. The latter method is only valid for stationary measurements, whereas the scanning lidar measurements are moving spatially. Their approach does not directly incorporate the influence of the spatial
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