Overcoming Blade Interference: A Gappy-POD Data Reconstruction Method for Nacelle-Mounted Lidar Measurements

Nacelle-mounted lidar systems suffer data loss due to unfavourable atmospheric conditions such as rain or fog and most importantly the rotation of the blades that obstruct the laser beam from measuring upstream of the turbine. In this paper, we apply Gappy Proper Orthogonal Decomposition (Gappy-POD)...

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Bibliographic Details
Published in:Journal of physics. Conference series 2022-05, Vol.2265 (2), p.22078
Main Authors: Kidambi Sekar, A. P., van Dooren, M. F., Kühn, M.
Format: Article
Language:English
Subjects:
Data integrity
Data loss
Fog
Interpolation
Large eddy simulation
Laser beams
Lidar
Misalignment
Missing data
Nacelles
Physics
Proper Orthogonal Decomposition
Reconstruction
Turbines
Yaw
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Summary:Nacelle-mounted lidar systems suffer data loss due to unfavourable atmospheric conditions such as rain or fog and most importantly the rotation of the blades that obstruct the laser beam from measuring upstream of the turbine. In this paper, we apply Gappy Proper Orthogonal Decomposition (Gappy-POD) to reconstruct incomplete flow fields from nacelle-mounted lidar measurements. For this purpose, two scanning nacelle-based SpinnerLidar simulations are performed inside a Large Eddy Simulation, one measuring the undisturbed wind inflow and the other in the wake of a reference turbine. Data loss of up to 90 % is simulated by artificially removing measurement points. The performance of Gappy-POD in reconstructing the wind fields is evaluated by comparing metrics such as effective wind speeds, vertical shear, yaw misalignment, wake deficit, wake meandering and the turbulent spectra in fixed and rotating frames of reference. We see that Gappy-POD is capable of accurately reconstructing missing data in comparison to normally used spatial interpolation techniques even in cases where 90 % of the data was missing. As a result, the dynamics of the reconstructed wind fields can be investigated based on highly accurate lidar-based wind field retrievals. The methodology can be used as a tool to develop effective wind field reconstruction techniques from sparse data.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2265/2/022078