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.

Highlights

  • 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 simulations are used as a benchmark for comparing and quantifying the accuracy of the inflow model based on different reconstruction metrics such as the convergence of POD modes, eigenvalue distributions, velocity field reconstruction, wind field parameterisation and turbulent spectra in the fixed and rotational frames of reference

  • 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) and (3) the longitudinal wind component is assumed to be the main driver of the dynamics of turbine response dominating over the contributions of the lateral and vertical components when the turbine is aligned with the inflow

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Summary

Introduction

With recent advances in the field of lidar technology for wind energy applications, scanning and measurement of the inflow of wind turbines have attracted greater attention. Turbine-integrated lidar systems can scan wind fields upstream of the turbine, allowing these measurements to be incorporated into turbine operation and control. Using this information as an input, turbine 20 performance can be improved in terms of power production and reducing the structural loads by feed-forward lidar-assisted control. Substantial amount of research has been done on lidar-assisted wind turbine control following advances in photonicsbased communications for fibre-based lidar technologies that emerged during the early 2000’s (Harris et al, 2007). Simley et al (2018) provide an extensive overview of research on lidar inflow measurement based wind turbine control strategies. Turbine-mounted lidar measurements can be used for loads validation (Dimitrov et al, 2019) and characterization of tur bulence upstream of the rotor (Peña et al, 2017)

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