Wind field reconstruction from nacelle-mounted lidar short-range measurements

Borraccino, Antoine; Schlipf, David; Haizmann, Florian; Wagner, Rozenn

doi:10.5194/wes-2-269-2017

Cited by 52 publications

(46 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can be done by expressing

ū false(boldx false)

as a function of a and

ū_{\infty}

; thus, a appears as an additional variable in the field reconstruction fitting procedure. A suitable approach is to use some simple induction models as discussed in Troldborg and Forsting and Borraccino et al Note that this solution requires that measurements are taken at several different upwind distances. Here, we consider the 2‐D induction model from Troldborg and Forsting, which assumes both longitudinal and radial variation of the induced wind velocity.…”

Section: Reconstructing Wind Field Characteristicsmentioning

confidence: 99%

Wind turbine load validation using lidar‐based wind retrievals

et al. 2019

Self Cite

View full text Add to dashboard Cite

We define and demonstrate a procedure for carrying out wind turbine load validation based on measurements from nacelle‐mounted scanning lidars. Two coherent Doppler lidar systems, a pulsed lidar and a continuous‐wave lidar, are mounted on a 2.3‐MW wind turbine equipped with load measurement sensors. Wind measurements from a meteorological mast mounted at 2.5 rotor diameters distance are used as reference. The study shows how lidar measurements are processed and applied as inputs to aeroelastic load simulations, and the results are then compared with simulations where the wind inputs have been determined using the meteorological mast data in compliance with the IEC61400‐13 standard. For the majority of simulation cases considered, the use of nacelle‐mounted lidar measurements results in load estimation uncertainties lower or equal to those that are based on measurements from cup anemometers on the mast. These results demonstrate the usefulness of nacelle‐mounted lidars as tools for carrying out load validation without the need of meteorological masts.

show abstract

“…This can be done by expressing

ū false(boldx false)

as a function of a and

ū_{\infty}

Section: Reconstructing Wind Field Characteristicsmentioning

confidence: 99%

Wind turbine load validation using lidar‐based wind retrievals

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, in the present study we resort to a MC simulation as the main approach for covering the joint distribution of wind conditions. For assuring better and faster convergence, we use the low-discrepancy Halton sequence in a quasi-MC approach (Caflisch, 1998). While a crude MC integration has a convergence rate proportional to the square root of the number of samples N, i.e., the mean error ε ∝ N −0.5 , the convergence rate for a quasi-MC with a low-discrepancy sequence results in ε ∝ N −λ , 0.5 ≤ λ ≤ 1.…”

Section: Sampling Proceduresmentioning

confidence: 99%

From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases

et al. 2018

View full text Add to dashboard Cite

Abstract. We define and demonstrate a procedure for quick assessment of site-specific lifetime fatigue loads using simplified load mapping functions (surrogate models), trained by means of a database with high-fidelity load simulations. The performance of five surrogate models is assessed by comparing site-specific lifetime fatigue load predictions at 10 sites using an aeroelastic model of the DTU 10 MW reference wind turbine. The surrogate methods are polynomial chaos expansion, quadratic response surface, universal Kriging, importance sampling, and nearest-neighbor interpolation. Practical bounds for the database and calibration are defined via nine environmental variables, and their relative effects on the fatigue loads are evaluated by means of Sobol sensitivity indices. Of the surrogate-model methods, polynomial chaos expansion provides an accurate and robust performance in prediction of the different site-specific loads. Although the Kriging approach showed slightly better accuracy, it also demanded more computational resources.

show abstract

“…A similar methodology can be applied to scanning lidar measurements of different types of complex flows such as the induction zone upstream a wind turbine rotor, wind turbine wakes and low level jets and or various conditions of atmospheric stratification [28,48,[51][52][53][54]. Physical models can also be used to reconstruct the wind field from lidar measurements.…”

Section: Lidar In Complex Flowmentioning

confidence: 99%

“…Wind lidar's ability to measure wind profiles to greater heights than is possible with conventional met towers, to repeatedly sample large swathes using scanning lidars, and to retrieve multiple wind vectors from a single point using coordinated scanning lidars [47] have made them a popular choice for measuring flow over complex terrain [13], in turbine wakes, in the inflow to a turbine, and in urban areas [11,28,48]. These complex flows exhibit spatial heterogeneity and transient features introduced by terrain, patchy land cover, turbine or structural wakes, local meteorology, and other effects.…”

Section: Lidar In Complex Flowmentioning

confidence: 99%

See 1 more Smart Citation

IEA Wind Task 32: Wind Lidar Identifying and Mitigating Barriers to the Adoption of Wind Lidar

Clifton

Clive²,

Gottschall

et al. 2018

Remote Sensing

Self Cite

View full text Add to dashboard Cite

IEA Wind Task 32 exists to identify and mitigate barriers to the adoption of lidar for wind energy applications. It leverages ongoing international research and development activities in academia and industry to investigate site assessment, power performance testing, controls and loads, and complex flows. Since its initiation in 2011, Task 32 has been responsible for several recommended practices and expert reports that have contributed to the adoption of ground-based, nacelle-based, and floating lidar by the wind industry. Future challenges include the development of lidar uncertainty models, best practices for data management, and developing community-based tools for data analysis, planning of lidar measurements and lidar configuration. This paper describes the barriers that Task 32 identified to the deployment of wind lidar in each of these application areas, and the steps that have been taken to confirm or mitigate the barriers. Task 32 will continue to be a meeting point for the international wind lidar community until at least 2020 and welcomes old and new participants.

show abstract

Wind field reconstruction from nacelle-mounted lidar short-range measurements

Cited by 52 publications

References 18 publications

Wind turbine load validation using lidar‐based wind retrievals

Wind turbine load validation using lidar‐based wind retrievals

From wind to loads: wind turbine site-specific load estimation with surrogate models trained on high-fidelity load databases

IEA Wind Task 32: Wind Lidar Identifying and Mitigating Barriers to the Adoption of Wind Lidar

Contact Info

Product

Resources

About