Abstract
This paper presents a method for reconstructing the wake wind field of a wind turbine based on planar light detection and ranging (LiDAR) scans crossing the wake transversally in the vertical and horizontal directions. Volumetric measurements enable the study of wake characteristics in these two directions. Due to a lack of highly resolved wind speed measurements as reference data, we evaluate the reconstruction in a synthetic environment and determine the reconstruction errors. The wake flow of a multi-megawatt wind turbine is calculated within a 10-min large-eddy simulation (LES) for high-thrust loading conditions. We apply a numerical LiDAR simulator to this wake wind field to achieve realistic one-dimensional velocity data. We perform a nacelle-based set-up with combined plan position indicator and range height indicator scans with eight scanning velocities each. We temporally up-sample the synthetic LiDAR data with a weighted combination of forward- and backward-oriented space–time conversion to retrospectively extract high-resolution wake characteristic dynamics. These dynamics are used to create a dynamic volumetric wake deficit. Finally, we reconstruct the dynamic wake wind field in three spatial dimensions by superposing an ambient wind field with the dynamic volumetric wake deficit. These results demonstrate the feasibility of wake field reconstruction using long-range LiDAR measurements.
Highlights
The ongoing trend toward increased rotor diameters and decreased relative spacing of turbines in wind farms indicates that wind turbine manufacturers, wind farm operators, and researchers need a better understanding of wake-induced load generation
This paper presents a method for reconstructing the wake wind field of a wind turbine based on planar light detection and ranging (LiDAR) scans crossing the wake transversally in the vertical and horizontal directions
Such models are based on the correlation of statistical analysis of wind turbine operational data, better known as supervisory control and data acquisition (SCADA) data, load measurements, and time-averaged wind speed point measurements, such as those taken at meteorological towers or the turbine nacelle itself
Summary
The ongoing trend toward increased rotor diameters and decreased relative spacing of turbines in wind farms indicates that wind turbine manufacturers, wind farm operators, and researchers need a better understanding of wake-induced load generation. Because of the limited ability to identify and resolve spatial wake structures with conventional or ultrasonic anemometers, traditional wake models [5,6,7] can only be validated using long-term temporally averaged data corresponding to a simplified steady-flow state over 10-min periods. Such models are based on the correlation of statistical analysis of wind turbine operational data, better known as supervisory control and data acquisition (SCADA) data, load measurements, and time-averaged wind speed point measurements, such as those taken at meteorological towers or the turbine nacelle itself. Steady wake models describe the wake as stationary and in a fixed reference frame (FFoR), which does not explicitly resolve the dynamic effects [5,6]
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