LIDAR Wind Speed Measurements of Evolving Wind Fields

Abstract

Light Detection and Ranging (LIDAR) systems are able to measure the speed of incoming wind before it interacts with a wind turbine rotor. These preview wind measurements can be used in feedforward control systems designed to reduce turbine loads. However, the degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. Past studies have assumed Taylor's frozen turbulence hypothesis, which implies that turbulence remains unchanged as it advects downwind at the mean wind speed. With Taylor's hypothesis applied, the only source of wind speed measurement error is distortion caused by the LIDAR. This study introduces wind evolution, characterized by the longitudinal coherence of the wind, to LIDAR measurement simulations to create a more realistic measurement model. A simple model of wind evolution is applied to a frozen wind field used in previous studies to investigate the effects of varying the intensity of wind evolution. Simulation results show the combined effects of LIDAR errors and wind evolution for realistic turbine-mounted LIDAR measurement scenarios.Nomenclature a decay parameter for exponential coherence a l decrement parameter for transverse coherence (l ∈ {u, v, w}) b l offset parameter for transverse coherence (l ∈ {u, v, w}) D longitudinal distance between two points or measurement preview distance F focal distance f frequency (Hz) θ LIDAR measurement angle off of longitudinal direction k wind velocity wavenumber (m −1 ) λ wavelength (m) R range along LIDAR beam r scan radius for spinning LIDAR scenario r i,j distance between two points in the yz plane U mean wind speed (m/s) u i,j average mean wind speed between two points in the yz plane ψ azimuth angle in the rotor plane γ 2 xy (f ) Coherence between signals x and y

Assessment of virtual towers performed with scanning wind lidars and Ka-band radars during the XPIA experiment

“…The increasing need of monitoring the atmospheric boundary layer for a broad range of technological and scientific pursuits -such as for meteorology (Banta et al, 2002;Calhoun et al, 2006;Emeis et al, 2007;Horanyi et al, 2015;Vanderwende et al, 2015;Bonin et al, 2015), renewable energy (Thresher et al, 2008;Jones and Bouamane, 2011;Aitken et al, 2014;Iungo, 2016) and air traffic management (George and Yang, 2012;Smalikho and Banakh, 2015) -has led to a rapid development of remotesensing measurement techniques, such as wind lidars Cariou, 2015;Simley and Pao, 2012;Iungo andPorté-Agel, 2013, 2014) and radars (Farnet and Stevens, 1990;O'Hora and Bech, 2007;Hirth and Schroeder, 2013;Hirth et al, 2015). Compared to classical meteorological towers, remote-sensing instruments allow easier deployment, enhanced capability of varying deployment locations and potentially lower costs.…”

Reducing LIDAR wind speed measurement error with optimal filtering

“…For both scenarios, the PSDs of the effective wind speeds at the turbine and the LIDAR measurements, as well as the CPSD between the two signals are calculated based on the spectral model of the wind field and the LIDAR and rotor properties using techniques outlined in [8] and [19]. The chosen preview distance of d = 90 m is near optimal for estimation of rotor effective wind speed, but is far from the optimal preview distance of d = 170 m found for estimating blade effective wind speed in [19].…”

“…Coherence is very high at low frequencies then decays to zero as frequency increases, mostly due to wind evolution, which causes higher frequencies of turbulence to decay more quickly than the low frequency structures. A detailed explanation of the sources of coherence loss is provided in [8].…”

“…Distortion caused by the LIDAR measurement [7] and the evolution of the wind [8] as it advects toward the turbine both cause measurement error. In the past few years, controller designs based on simulation have begun to incorporate realistic LIDAR models to introduce the spatial averaging effect of LIDAR as well as the detection of transverse and vertical wind speeds through line-of-sight velocity This work was supported in part by the US National Renewable Energy Laboratory.…”

“…Taylor's frozen turbulence hypothesis, which describes turbulence as remaining unchanged while traveling at the mean wind speed [9], is usually assumed for simulation purposes. However, wind evolution, which is described by a spatial coherence loss in wind speeds at upstream and downstream locations [8]- [10], is beginning to appear in feedforward control simulations [11]. An additional source of error between LIDAR measurements and rotor wind speeds is the spatial averaging caused by the large wind turbine rotor area.…”