An error reduction algorithm to improve lidar turbulence estimates for wind energy

Abstract

Abstract. Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry.In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices.L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physicsbased corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI) error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.

Comparison of Methods to Derive Radial Wind Speed from a Continuous-Wave Coherent Lidar Doppler Spectrum

“…A machine-learning approach to produce unfiltered wind speed variances from pulsed lidar signals was used in Newman and Clifton (2017). Besides a model for the spatial averaging the algorithm also includes automatic noise removal.…”

Floating lidar as an advanced offshore wind speed measurement technique: current technology status and gap analysis in regard to full maturity

“…43 In order to derive meaningful TI values from lidar data though, several approaches have been proposed. 44,45 A check of their application to FLS data and their combination with the existing motion correction algorithms has yet to be performed.…”

Wind–Temperature Regime and Wind Turbulence in a Stable Boundary Layer of the Atmosphere: Case Study

“…The lidar methods and results of the lidar studies of the ABL, the height of the turbulent mixing layer at different temperature stratifications, and the different types of underlying surfaces are discussed in the literature [11][12][13][14][15]. The capabilities of the lidar methods in the studies of wind turbulence and the results of their testing in atmospheric experiments are discussed in the literature [16][17][18][19][20][21][22][23][24][25][26][27].…”

“…Fewer theoretical works are devoted to study of the structure of a stable ABL. Despite the obtained theoretical results and a large number of works devoted to experimental lidar studies of the atmospheric boundary layer [16][17][18][19][20][21][22][23][24][25][26][27], it remains unclear whether the stable boundary layer of the atmosphere has a layered structure such as that of the upper atmosphere.…”

“…For that, in the measurements, we employed coherent Doppler wind lidar concurrently with a microwave temperature profiler. As a result, we succeeded in not only obtaining data on the wind processes but also, in contrast to the lidar experiments on ABLs [16][17][18][19][20][21][22][23][24][25][26][27], relating them with the temperature regime in the ABL.…”