Preprints
https://doi.org/10.5194/wes-2021-16
https://doi.org/10.5194/wes-2021-16

  10 Mar 2021

10 Mar 2021

Review status: a revised version of this preprint is currently under review for the journal WES.

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

Anantha Padmanabhan Kidambi Sekar, Marijn Floris van Dooren, Andreas Rott, and Martin Kühn Anantha Padmanabhan Kidambi Sekar et al.
  • ForWind, Institue of Physics, University of Oldenburg, Küpkersweg 70, 26129 Oldenburg, Germany

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.

Anantha Padmanabhan Kidambi Sekar et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-16', Wim Munters, 07 Apr 2021
  • RC2: 'Comment on wes-2021-16', Anonymous Referee #2, 27 Apr 2021
  • AC1: 'Reply to reviewers', Anantha Padmanabhan Kidambi Sekar, 08 Jul 2021

Anantha Padmanabhan Kidambi Sekar et al.

Anantha Padmanabhan Kidambi Sekar et al.

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Short summary
Turbine-mounted lidars performing inflow scans can be used to optimise wind turbine performance and extend their lifetime. This paper introduces a new method to extract wind inflow information from a turbine-mounted scanning SpinnerLidar based on Proper Orthogonal Decomposition. This method offers a balance between simple reconstruction methods and complicated physics-based solvers. The results show that the model can be used for lidar assisted control, loads validation and turbulence studies.