Evaluation of the LINCOM wind field reconstruction method with simulations and full-scale measurements

Sekar, Anantha Padmanabhan Kidambi; Dooren, Marijn Floris van; Mikkelsen, Torben; Sjöholm, Mikael; Astrup, Poul; Kühn, Martin

doi:10.1088/1742-6596/1037/5/052008

Cited by 7 publications

(6 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our method provides a trade-off between conventional wind field reconstructions that require strong wind field assumptions and time-consuming physics-based solvers. Even though this study could benefit from full 3D wind fields, POD analysis of the line-of-sight measurements from a turbine-mounted lidar capable of high spatial coverage is considered reasonable because (1) the line-of-sight measurements are dominated by the longitudinal velocity component for turbine-mounted lidars (Harris et al, 2007): (2) it is quite challenging to extract the full 3D wind field information from line-of-sight measurements (Kidambi Sekar et al, 2018) of turbine response dominating over the contributions of the lateral and vertical components when the turbine is aligned with the inflow.…”

Section: Discussionmentioning

confidence: 99%

“…Guillemin et al (2018) presented means of extracting real time 3D wind field parameters using a recursive weighted least-squares method validated with simulated pulsed lidar measurements. Kidambi Sekar et al (2018) investigated the performance of LINCOM (a fast Navier-Stokes physics-based solver) to instantaneously reconstruct the local 3D velocity components from line-of-sight measurements of a SpinnerLidar on a spherical measurement plane upstream of the rotor.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Modelling the Wind Turbine Inﬂow with a Reduced Order Model based on SpinnerLidar Measurements

Sekar

Dooren

Rott

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Preview measurements of the inflow by turbine-mounted lidar systems can be used to optimise wind turbine performance by increasing power production and alleviating structural loads. Here, we apply Proper Orthogonal Decomposition (POD) to the line-of-sight wind speed measurements of a SpinnerLidar obtained from a large eddy simulation of a wind turbine operating in a turbulent atmospheric boundary layer. The aim of this work was to identify the dominant POD modes to derive a reduced order representation of the turbine inflow without making strong assumptions about the flow field. This dimensional reduction is a first step towards the development of a reduced order inflow model (ROM) that offers a trade-off between wind field reconstruction techniques requiring flow assumptions and more complex physics-based representations. We found that only a few modes are required to capture the dynamics of the wind field parameters commonly used for lidar assisted wind turbine control such as the effective wind speed, vertical shear, directional misalignment. A possible interpretation of the modes is presented by direct comparison with these wind field parameters. Evaluating six different metrics in the time and frequency domains related to the spatial, frequency domain and energy quantities, we find that a 10 mode ROM could accurately describe most spatio-temporal variations in the inflow. The reduced order modelling was accomplished using the inherent volume averaging property of lidar devices that attenuates high frequency turbulence with lower importance for the overall turbine response thus allowing significant data compression. Based on the models inflow wind field reconstruction performance, this method has potential use for lidar-assisted control, loads validation and turbulence characterisation.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Modelling the Wind Turbine Inﬂow with a Reduced Order Model based on SpinnerLidar Measurements

Sekar

Dooren

Rott

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The latter is defined as the Fourier transform of the specific sampling lattice. The 1D version of this theorem is the wellknown Shannon-Nyquist theorem (Shannon, 1984). An application of this theorem to nonuniform distributed samples, like those measured by remote sensing instruments, is unfeasible due to the lack of periodicity of the sampling points.…”

Section: The Barnes Objective Analysis -Fundamentals and Extension Tomentioning

confidence: 99%

LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 1: Theoretical framework

2021

View full text Add to dashboard Cite

Abstract. A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and retrieval of the velocity statistical moments is proposed. LiSBOA represents an adaptation of the classical Barnes scheme for the statistical analysis of unstructured experimental data in N-dimensional space, and it is a suitable technique for the evaluation over a structured Cartesian grid of the statistics of scalar fields sampled through scanning lidars. LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. This revisited theoretical framework for the Barnes objective analysis enables the formulation of guidelines for the optimal design of lidar experiments and efficient application of LiSBOA for the postprocessing of lidar measurements. The optimal design of lidar scans is formulated as a two-cost-function optimization problem, including the minimization of the percentage of the measurement volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field. The optimal design of the lidar scans also guides the selection of the smoothing parameter and the total number of iterations to use for the Barnes scheme. LiSBOA is assessed against a numerical data set generated using the virtual lidar technique applied to the data obtained from a large eddy simulation (LES). The optimal sampling parameters for a scanning Doppler pulsed wind lidar are retrieved through LiSBOA, and then the estimated statistics are compared with those of the original LES data set, showing a maximum error of about 4 % for both mean velocity and turbulence intensity.

show abstract

“…CC BY 4.0 License. Newsom and Banta, 2004), optimal interpolation (Xu and Gong, 2002;Kongara et al, 2012), least-squares methods (Newsom et al, 2008), Navier-Stokes solvers (Astrup et al, 2017;Sekar et al, 2018) were applied for the reconstruction of the velocity vector field from dual-Doppler measurements.…”

Section: Introductionmentioning

confidence: 99%

LiSBOA: LiDAR Statistical Barnes Objective Analysis for optimal design of LiDAR scans and retrieval of wind statistics. Part I: Theoretical framework

Letizia¹,

Zhan²,

Iungo³

2020

Preprint

View full text Add to dashboard Cite

Abstract. A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for optimal design of LiDAR scans and retrieval of the velocity statistical moments is proposed. The LiSBOA represents an adaptation of the classical Barnes scheme for the statistical analysis of unstructured experimental data in N-dimensional spaces and it is a suitable technique for the evaluation over a structured Cartesian grid of the statistics of scalar fields sampled through scanning LiDARs. The LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. This revisited theoretical framework for the Barnes objective analysis enables the formulation of guidelines for optimal design of LiDAR experiments and efficient application of the LiSBOA for the post-processing of LiDAR measurements. The optimal design of LiDAR scans is formulated as a two cost-function optimization problem including the minimization of the percentage of the measurement volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field. The optimal design of the LiDAR scans also guides the selection of the smoothing parameter and the total number of iterations to use for the Barnes scheme.

show abstract

Evaluation of the LINCOM wind field reconstruction method with simulations and full-scale measurements

Cited by 7 publications

References 2 publications

Modelling the Wind Turbine Inﬂow with a Reduced Order Model based on SpinnerLidar Measurements

Modelling the Wind Turbine Inﬂow with a Reduced Order Model based on SpinnerLidar Measurements

LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 1: Theoretical framework

LiSBOA: LiDAR Statistical Barnes Objective Analysis for optimal design of LiDAR scans and retrieval of wind statistics. Part I: Theoretical framework

Contact Info

Product

Resources

About