Wind Predictions Upstream Wind Turbines from a LiDAR Database

This article presents a new method to predict the wind velocity upstream a horizontal axis wind turbine from a set of light detection and ranging (LiDAR) measurements. The method uses higher order dynamic mode decomposition (HODMD) to construct a reduced order model (ROM) that can be extrapolated in space. LiDAR measurements have been carried out upstream a wind turbine at six different planes perpendicular to the wind turbine axis. This new HODMD-based ROM predicts with high accuracy the wind velocity during a timespan of 24 h in a plane of measurements that is more than 225 m far away from the wind turbine. Moreover, the technique introduced is general and obtained with an almost negligible computational cost. This fact makes it possible to extend its application to both vertical axis wind turbines and real-time operation.

[1]  D Letalick,et al.  All-Fiber Multifunction Continuous-Wave Coherent Laser Radar at 1.55 num for Range, Speed, Vibration, and Wind Measurements. , 2000, Applied optics.

[2]  Soledad Le Clainche,et al.  Accelerating oil reservoir simulations using POD on the fly , 2017 .

[3]  Daeyoung Kim,et al.  A comparison of ground-based LiDAR and met mast wind measurements for wind resource assessment over various terrain conditions , 2016 .

[4]  F. Takens Detecting strange attractors in turbulence , 1981 .

[5]  Alan Wright,et al.  Disturbance Accommodating Control Design for Wind Turbines Using Solvability Conditions , 2017 .

[6]  Kathryn E. Johnson,et al.  Lidar-assisted wind turbine feedforward torque controller design below rated , 2014, 2014 American Control Conference.

[7]  Soledad Le Clainche,et al.  Higher order dynamic mode decomposition of noisy experimental data: The flow structure of a zero-net-mass-flux jet , 2017 .

[8]  Soledad Le Clainche Martínez,et al.  Spatio-Temporal Koopman Decomposition , 2018, J. Nonlinear Sci..

[9]  Michael Harris,et al.  Advance measurement of gusts by laser anemometry , 2007 .

[10]  Kathryn E. Johnson,et al.  Preventing wind turbine overspeed in highly turbulent wind events using disturbance accommodating control and light detection and ranging , 2015 .

[11]  David Schlipf,et al.  Field Testing LIDAR Based Feed-Forward Controls on the NREL Controls Advanced Research Turbine , 2013 .

[12]  V A Banakh,et al.  Representativeness of wind measurements with a cw Doppler lidar in the atmospheric boundary layer. , 1995, Applied optics.

[13]  José M. Vega,et al.  LUPOD: Collocation in POD via LU decomposition , 2017, J. Comput. Phys..

[14]  Rozenn Wagner,et al.  Accounting for the speed shear in wind turbine power performance measurement , 2011 .

[15]  Soledad Le Clainche Martínez,et al.  Higher Order Dynamic Mode Decomposition , 2017, SIAM J. Appl. Dyn. Syst..

[16]  G. Stewart Error and Perturbation Bounds for Subspaces Associated with Certain Eigenvalue Problems , 1973 .

[17]  Alan Wright,et al.  Design of Controls to Attenuate Loads in the Controls Advanced Research Turbine , 2004 .

[18]  Torben Mikkelsen,et al.  Lidar wind speed measurements from a rotating spinner , 2010 .

[19]  L. Sirovich Turbulence and the dynamics of coherent structures. I. Coherent structures , 1987 .

[20]  Xiong Yu,et al.  LiDAR technology for wind energy potential assessment: Demonstration and validation at a site around Lake Erie , 2017 .

[21]  H. Jørgensen,et al.  Wind lidar evaluation at the Danish wind test site in Høvsøre , 2006 .

[22]  Soledad Le Clainche,et al.  New Robust Method to Study Flight Flutter Testing , 2019 .

[23]  S. Gryning,et al.  Offshore wind profiling using light detection and ranging measurements , 2009 .

[24]  Gene H. Golub,et al.  Matrix computations , 1983 .

[25]  P. Schmid,et al.  Dynamic mode decomposition of numerical and experimental data , 2008, Journal of Fluid Mechanics.

[26]  Alan Wright,et al.  Field Testing of Feedforward Collective Pitch Control on the CART2 Using a Nacelle-Based Lidar Scanner , 2014 .

[27]  Ioannis Antoniou,et al.  Power curve measurement with a nacelle mounted lidar , 2013 .

[28]  Zhizhen Zhao,et al.  Spatiotemporal Feature Extraction with Data-Driven Koopman Operators , 2015, FE@NIPS.