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Abstract
Measurement campaigns in wind energy research are becoming increasingly complex, which has exacerbated the difficulty of taking optimal measurements using light detection and ranging (LiDAR) systems. Compromises between spatial and temporal resolutions are always necessary in the study of heterogeneous flows, like wind turbine wakes. Below, we develop a method for space-time conversion that acts as a temporal fluid-dynamic interpolation without incurring the immense computing costs of a 4D flow solver. We tested this space-time conversion with synthetic LiDAR data extracted from a large-eddy-simulation (LES) of a neutrally stable single-turbine wake field. The data was synthesised with a numerical LiDAR simulator. Then, we performed a parametric study of 11 different scanning velocities. We found that temporal error dominates the mapping error at low scanning speeds and that spatial error becomes dominant at fast scanning speeds. Our space-time conversion method increases the temporal resolution of the LiDAR data by a factor 2.4 to 40 to correct the scan-containing temporal shift and to synchronise the scan with the time code of the LES data. The mean-value error of the test case is reduced to a minimum relative error of 0.13% and the standard-deviation error is reduced to a minimum of 0.6% when the optimal scanning velocity is used. When working with the original unprocessed LiDAR measurements, the space-time-conversion yielded a maximal error reduction of 69% in the mean value and 58% in the standard deviation with the parameters identified with our analysis.
Highlights
The arrangement of wind turbines into dense and efficient clusters is a central challenge in the design of wind farms
This paper has presented a space-time conversion method for long-range planar light detection and ranging (LiDAR) data, which achieves temporal interpolation that reflects reasonable approximations of fluid-dynamic processes
We used synthetic LiDAR data generated by a numerical simulator and in an LES wind-turbine wake flow field to evaluate the method in terms of the mean wind speed and standard deviation
Summary
The arrangement of wind turbines into dense and efficient clusters is a central challenge in the design of wind farms. The detrimental effect of wake shading decreases energy yield and increases dynamic loads [1,2]. These additional loads lead to increased fatigue on the turbine components, increasing the likelihood of failure and early maintenance [3,4]. This additional and unexpected maintenance will deteriorate the economic and energy efficiency of the wind farm [5]. Since single-point measurements are limited to cup anemometers mounted on the turbines themselves or on meteorological masts, reference and validation data can usually only be applied in the form of time averages [6,7,8]
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