2D VAR single Doppler lidar vector retrieval and its application in offshore wind energy

Cherukuru, Nihanth W.; Calhoun, Ronald; Krishnamurthy, Raghavendra; Svardal, Benny; Reuder, Joachim; Flügge, Martin

doi:10.1016/j.egypro.2017.10.378

Cited by 8 publications

(19 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The wind field utilized in this work was retrieved by a new proposed two-dimensional variational analysis method (2D-VAR) from the measurements collected by a Lockheed Martin Coherent Technologies (LMCT) WindTracer ® Doppler lidar (Louisville, CO, USA) during 25-27 June 2014, at Tehachapi, California [12]. The specification of the Doppler lidar is listed in Table 1.…”

Section: Data Information and Wind Field Retrievalmentioning

confidence: 99%

“…The data was collected in a horizontal plane at 1453 m ASL which included the height of the lidar system (3 m). The 2D-VAR method used in the present work is based on a variational parameter identification formulation [12]. The method involves finding the best fit 2D wind velocity vector (X) which minimizes a cost function:…”

Section: Data Information and Wind Field Retrievalmentioning

confidence: 99%

See 1 more Smart Citation

Wind Gust Detection and Impact Prediction for Wind Turbines

Zhou

Cherukuru

Sun³

et al. 2018

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Wind gusts on a scale from 100 m to 1000 m are studied due to their significant influence on wind turbine performance. A detecting and tracking algorithm is proposed to extract gusts from a wind field and track their movement. The algorithm utilizes the "peak over threshold method," Moore-Neighbor tracing algorithm, and Taylor's frozen turbulence hypothesis. The algorithm was implemented for a three-hour, two-dimensional wind field retrieved from the measurements of a coherent Doppler lidar. The Gaussian shape distribution of the gust spanwise deviation from the streamline was demonstrated. Size dependency of gust deviations is discussed, and an empirical power function is derived. A prediction model estimating the impact of gusts with respect to arrival time and the probability of arrival locations is introduced, in which the Gaussian plume model and random walk theory including size dependency are applied. The prediction model was tested and the results reveal that the prediction model can represent the spanwise deviation of the gusts and capture the effect of gust size. The prediction model was applied to a virtual wind turbine array, and estimates are given for which wind turbines would be impacted.

show abstract

Section: Data Information and Wind Field Retrievalmentioning

confidence: 99%

Section: Data Information and Wind Field Retrievalmentioning

confidence: 99%

Wind Gust Detection and Impact Prediction for Wind Turbines

Zhou

Cherukuru

Sun³

et al. 2018

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In variational methods, weak or penalty constraints can be directly added to the cost function. According to models that compute analytical variables x in Equation (A14), variational methods can be classified into two types [186]: function fitting-based methods, e.g., two-step variational method (2SVAR); and physical-based method, e.g., four-dimensional variational data assimilation (4DVAR).…”

Section: Variational Methodsmentioning

confidence: 99%

“…The last term is a conservation constraint [93]. The weights in Equation (A17) are chosen to ensure all the terms having the same order of magnitudes [186]. To model velocity components (u, v), the second order orthonormal functions are employed:…”

Section: Variational Methodsmentioning

confidence: 99%

A Review of Progress and Applications of Pulsed Doppler Wind LiDARs

et al. 2019

View full text Add to dashboard Cite

Doppler wind LiDAR (Light Detection And Ranging) makes use of the principle of optical Doppler shift between the reference and backscattered radiations to measure radial velocities at distances up to several kilometers above the ground. Such instruments promise some advantages, including its large scan volume, movability and provision of 3-dimensional wind measurements, as well as its relatively higher temporal and spatial resolution comparing with other measurement devices. In recent decades, Doppler LiDARs developed by scientific institutes and commercial companies have been well adopted in several real-life applications. Doppler LiDARs are installed in about a dozen airports to study aircraft-induced vortices and detect wind shears. In the wind energy industry, the Doppler LiDAR technique provides a promising alternative to in-situ techniques in wind energy assessment, turbine wake analysis and turbine control. Doppler LiDARs have also been applied in meteorological studies, such as observing boundary layers and tracking tropical cyclones. These applications demonstrate the capability of Doppler LiDARs for measuring backscatter coefficients and wind profiles. In addition, Doppler LiDAR measurements show considerable potential for validating and improving numerical models. It is expected that future development of the Doppler LiDAR technique and data processing algorithms will provide accurate measurements with high spatial and temporal resolutions under different environmental conditions.

show abstract

“…Zhou et al [1] provided an excellent example of the value of remote observations of two-component wind fields in the lower atmospheric boundary layer. They obtained two-component wind fields by applying a 2D variational (2D-VAR) method [2] to single component velocity data from a single Doppler lidar. While their paper mentions cross-correlation (CC) and wavelet-based optical flow (WOF) algorithms as alternative ways to obtain such wind fields, statements about these two methods are inaccurate and do not consider the latest published research on this subject.…”

Section: Introductionmentioning

confidence: 99%

Comments on “Wind Gust Detection and Impact Prediction for Wind Turbines”

Mayor

Dérian

2018

Remote Sensing

View full text Add to dashboard Cite

We refute statements in “Zhou, K., et al. Wind gust detection and impact prediction for wind turbines. Remote Sens. 2018, 10, 514.” the impracticality of motion estimation methods to derive two-component vector wind fields from single scanning aerosol lidar data. Our assertion is supported by recently published results on the performance of two image-based motion estimation methods: cross-correlation (CC) and wavelet-based optical flow (WOF). The characteristics and performances of CC and WOF are compared with those of a two-dimensional variational (2D-VAR) method that was applied to radial velocity fields from a single scanning Doppler lidar. The algorithmic aspects of WOF and 2D-VAR are reviewed and we conclude that these two approaches are in fact similar and practical.

show abstract

2D VAR single Doppler lidar vector retrieval and its application in offshore wind energy

Cited by 8 publications

References 19 publications

Wind Gust Detection and Impact Prediction for Wind Turbines

Wind Gust Detection and Impact Prediction for Wind Turbines

A Review of Progress and Applications of Pulsed Doppler Wind LiDARs

Comments on “Wind Gust Detection and Impact Prediction for Wind Turbines”

Contact Info

Product

Resources

About