Abstract
Doppler LiDARs have become flexible and versatile remote sensing devices for wind energy applications. The possibility to measure radial wind speed components contemporaneously at multiple distances is an advantage with respect to meteorological masts. However, these measurements must be filtered due to the measurement geometry, hard targets and atmospheric conditions. To ensure a maximum data availability while producing low measurement errors, we introduce a dynamic data filter approach that conditionally decouples the dependency of data availability with increasing range. The new filter approach is based on the assumption of self-similarity, that has not been used so far for LiDAR data filtering. We tested the accuracy of the dynamic data filter approach together with other commonly used filter approaches, from research and industry applications. This has been done with data from a long-range pulsed LiDAR installed at the offshore wind farm ‘alpha ventus’. There, an ultrasonic anemometer located approximately 2.8 km from the LiDAR was used as reference. The analysis of around 1.5 weeks of data shows, that the error of mean radial velocity can be minimised for wake and free stream conditions.
Highlights
The basis of any empirical work, whether in the commercial or scientific context, is data that have been acquired through a measurement process
It can be seen that data points below the red line indicating a –24 dB level have high deviations in the range of –32 m/s to 32 m/s wind speed, we assume that the points are invalid
In order to distinguish between those, the dynamic filtering approach is based on two subsequent process steps, temporal & spatial normalisation and data-density calculation
Summary
The basis of any empirical work, whether in the commercial or scientific context, is data that have been acquired through a measurement process. Meyer Forsting et al [18] investigated the adaption of a despiking method from stationary to scanning situations and took an important step towards the filtering of scanned LiDAR measurements Those methods were not designed for an application in LiDAR remote sensing and represent more or less a best practice for general time series processing. The CNR-threshold filter is based on the accuracy of the radial velocity with respect to the CNR, the interquartile-range filter is based on the data distribution and the standard deviation filters on the assumption of normal distribution All these assumptions, do not rely on factors which affect LiDAR measurements. We introduce a highly self-adapting methodology that demonstrate how line-of-sight velocity measurements of pulsed long-range LiDAR devices can be filtered dynamically to maximise accuracy and data availability of mean radial velocities. Leosphere Windcube 200s data in the range of 2864 m has been carried out against ultrasonic anemometer data captured at an offshore meteorological mast in comparison to commonly established and research filters
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