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circle with a mean U = (u,v,w). The radial velocity in the direction of the laser beam, the radial wind speed vr, calculated at θ azimuth of the prism is the projection of U onto n: vr(θ) = n(θ)·U(n(θ)l −(0,0,z ′ )), (275) where z ′ is absolute position of the instrument a.g.l. if it is placed on an artificial elevation (e.g. tower). For ZephIR lidar, after calculating 60 points on the conical circle, all three velocity components can be obtained through a linear fit to trigonometric series as; u = b sinφ v = c sinφ a+bcosθ+csinθ, (276) w = a cosφ Θ = arctan v u . (277) For Windcube, radial wind speed vr from calculated at four measurement points are used in Eq. (266)–(268) directly to derive wind speed components and direction. 12.4 Experimental studies Conically scanning lidars assume the flow to be homogeneous in order to deduce the horizontal wind speed as it has been described in section 12.3.1. However, in moderately complex or complex terrain this assumption is not valid implying a risk that the lidar will derive an erroneous wind speed. The magnitude of this error was measured by collocating a meteorological mast and a lidar at two Greek sites, one hilly and one mountainous. In order to predict the error for various wind directions the flows at both sites were simulated with the linearised flow model LINCOM as described in section 12.3.2. The measurement data were compared with the model predictions with good results for the hilly site. In both experiments lidar data are collected by the standard QinetiQ software and synchronized with mast data by the CRES WindRose software. Instruments are calibrated according to the requirements of IEC61400-12-1:2005/Annex F and MEASNET guidelines at CRES Laboratory for Wind Turbine Testing. 12.4.1 Hilly site; Lavrio The Lavrio site is located 38 km SE of the center of Athens close to the coast of the Aegean Sea. The experiment took place between 2008-Dec-01and 2008-Jan-15. The highest point is 200 m ASL and main wind direction is 0 ◦ . The 100 m triangular lattice reference meteorological mast is equipped with cup anemometers and vanes at five heights (10, 32, 54, 76, and 100 m). Cups are to the east and vanes are to the west. There are also ultrasonic 3D Gill anemometers at three heights (34, 78, and 98 m) which are not used in this study due to problems with icing but this does not influence the used cup anemometers and vanes. Additionally, the temperature profile is measured using differential thermometers, as well as, the atmospheric pressure and the solar radiation. Dedicated instrumentation is used for signal protection, filtering and conditioning. The lidar is located nearly 12 m north of the mast. The measurement heights are 32 and 78 m. At Lavrio, most of the winds are northerly which means it is blowingfrom lidar to the mast. Thescatterplots(Figure 162-top)showgenerally5to 7%errors inwindspeed measurements. For the WAsP Engineering model we have used 3 km×3 km map with 4 m resolution simulating the wind direction from 0 ◦ to 360 ◦ with 6 ◦ bins. We have used all the data from the mast at each height and averaged them according to the wind direction in 10 ◦ bins. The comparison between the model and the measurements is shown in Figure 162 (lower two plots) and shows good correlation in some sectors. The mast is voluminous, thus the selected data must be far from boom direction which is 113 ◦ . These sectors are marked with lightgrey areasin the plotsfor ±30 ◦ . Theideal ratioline ofone is alsoshownand it represents 236 <strong>DTU</strong> Wind Energy-E-Report-0029(EN)
Figure 162: Lavrio: The scatter plots show generally 4% to 6% errors in wind speed measurements (top). Lower two plots are the comparison between the model and the measurement data for twodifferent heights. Small red dots are the error ratio for each 10 min measurement, big blue dots are the averaged 6 ◦ bins according to the wind direction and medium black dots are the model results. The mast shadow is marked with grey rectangles. The ideal ratio line of one, dashed blue, is also shown and it represents the cases where there is no difference between the lidar and the mast measurements. Especially for northerly directions the model predicts the lidar error well for both heights, while for the southerly directions the prediction is not so good. <strong>DTU</strong> Wind Energy-E-Report-0029(EN) 237
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Remote Sensing for Wind Energy DTU
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Author: Alfredo Peña, Charlotte B.
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4 Introduction to continuous-wave D
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8 Nacelle-based lidar systems 157 8
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12 Complex terrain and lidars 231 1
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1 Remote sensing of wind Torben Mik
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Figure 2: Calibration, laboratory w
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Figure 3: Example of scatter plots
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1.2.3 Summary of sodars Most of tod
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1.3.3 Wind lidars Measuring wind wi
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Figure 6: CW wind lidars (ZephIRs)
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Further developments Furthermore, n
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2 The atmospheric boundary layer S
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Figure 9: Large spatial scale varia
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Du3 Dt Du1 Dt Du2 Dt The three mome
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Figure 13: Consensus relations betw
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ψ z L ∼ − 5 L . For unstable c
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Figure 15: Behavior of the turbulen
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Figure 17: Newly developed models t
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The value of q0 at the surface is d
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the spray is the source of icing on
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u∗2 u∗1 u1(h) = u∗1 k ln h
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Figure 26:Land-seabreeze system,whe
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Figure28:Three dimensionalpicture o
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sufficient information. Finally, we
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Mann, J. (1998) Wind field simulati
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(2010) and comparison under differe
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To get the velocity field from the
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τ(k) [Arbitrary units] 10 3 10 2 1
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(Koopmans, 1974; Bendat and Piersol
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anemometer was installed at each en
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and fSw(f) u 2 ∗ = 1.05n 1+5.3n 5
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and n = 0.468. This spectrum implie
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to be calculated. We do that on a m
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Notation A Charnock constant neutra
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Maxey M. R. (1982) Distortion of tu
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4.2 Basic principles of lidar opera
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4.2.5 Wind profiling in conical sca
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4.3.1 Behaviour of scattering parti
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the beam radius at the output lens.
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from which the value of VLOS is der
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the atmosphere. The SNR 4 for a win
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individual line-of-sight wind speed
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A general approach to mitigating th
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from ±VH sinδ (if the tilt is tow
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as a down draught (of the same abso
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Table 7: Combined results from 28 Z
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in Eastern Jutland between January
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Figure 56: Normalized power curves
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the concept. Developments include i
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References x horizontal position in
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Wagner R., Mikkelsen T., and Courtn
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5.2 End-to-end description of pulse
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Scanner Coherent lidar measure the
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Figure 62: Radial wind velocity ret
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transform in order to use data obta
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This wavelength is also the most fa
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Eq.(132)isadaptedforcollimatedsyste
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5.3.6 Existing systems and actual p
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(Gottshall et al., 2010; Albers et
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References Albers A., Janssen A. W.
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derived from fluctuations of the wi
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Acoustic received echo (ARE) method
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Figure 71: Sample time-height cross
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A gradient minimum is characterized
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Figure73:Bragg-relatedacoustic(belo
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stability (inversion strength) can
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Figure 76: Combined soundingwith a
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Figure 79: Favorite regions (shaded
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Direct detection of MLH from acoust
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Engelbart D.A.M.and Bange J. (2002)
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7 What can remote sensing contribut
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uyms 9.0 8.5 8.0 7.5 7.0 120 140 16
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Bottom of rotor Φ rotation r w u
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Height Height Hub 1.6 1.4 1.2 1.0 0
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PP rated PP rated 1.0 0.8 0.6 0.4 0
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KEprofileKEhub 1.2 1.1 1.0 0.9 0.8
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PP rated WS Lidarms 1.0 0.8 0.6 0.4
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8 Nacelle-based lidar systems Andre
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• Flexibletrajectories.Dependingo
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Figure 104: Sketch of simultaneous
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The normal wind direction vector nw
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Figure 109: Test site at DTU Wind E
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Figure 112: Power curve met mast an
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Notation C number of sent photons C
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9 Lidars and wind turbine control -
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for three unknowns, it is impossibl
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model of the blade pitch actuator,
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|GRL| [-] 1 0.8 0.6 0.4 0.2 10 k [r
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PSD(Ωg) [(rpm) 2 /Hz] PSD(Ωg) [
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0.04 0.03 ˆk [ rad m ] 0.02 0.63 0
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[%] 10 0 −10 −20 −30 MyT Moop
Page 185 and 186: PSD(θ1) [rad 2 /Hz] PSD(Moop1) [Nm
Page 187 and 188: Pel/Pel,max [-] 1 0.98 0.96 0.94 0.
Page 189 and 190: fL weighting function GRL transfer
Page 191 and 192: E. Hau, Windkraftanlagen, 4th ed. S
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Page 195 and 196: vertical (m) 150 100 50 R d 0
Page 197 and 198: With feedback only, on the other ha
Page 199 and 200: Figure 136: Estimated preview requi
Page 201 and 202: Normalized C r = C r P Q 2 W (r) b
Page 203 and 204: Normalized C r = C r P Q 2 W (r) b
Page 205 and 206: Coherence 1 0.8 0.6 0.4 0.2 0 10
Page 207 and 208: Coherence 1 0.8 0.6 0.4 0.2 0 10
Page 209 and 210: Magnitude Squared 10 8 10 7 10 6 10
Page 211 and 212: Figure 148: During simulation, FAST
Page 213 and 214: Figure 149: Collective flap respons
Page 215 and 216: Magnitude (abs) blade pitch gen spe
Page 217 and 218: • Measurement coherence, which ca
Page 219 and 220: Jonkman, B. (2009) TurbSim user’s
Page 221 and 222: 11 Lidars and wind profiles Alfredo
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Page 225 and 226: z [−] zo 1 κ ln 40 38 36 34 32 3
Page 227 and 228: z [m] z [m] 1000 900 800 700 600 50
Page 229 and 230: the growth of the length scale, agr
Page 231 and 232: 12 Complex terrain and lidars Ferha
Page 233 and 234: Figure 158: The ZephIR models which
Page 235: Uconst wΑx l h Φ h.tanΦ Figure 1
Page 239 and 240: Figure 163: Panahaiko: The scatter
Page 241 and 242: References Albers A. and Janssen A.
Page 243 and 244: Wexler (1968), where the limitation
Page 245 and 246: 13.2.1 Systematic turbulence errors
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Page 249 and 250: The theoretical systematic errors a
Page 251 and 252: Height (m) 160 140 120 100 80 60 40
Page 253 and 254: Height (m) Height (m) 160 140 120 1
Page 255 and 256: RMSPE (%) 70 60 50 40 30 20 10 0 vu
Page 257 and 258: involves interaction of all compone
Page 259 and 260: Lindelöw P. (2007) Fibre Based Coh
Page 261 and 262: plications, including meteorologica
Page 263 and 264: Figure 173: Rayleigh-Jeans’ appro
Page 265 and 266: Figure 176: Brightness temperature
Page 267 and 268: with rain are shown in Fig. 179. A
Page 269 and 270: This method gives a good accuracy (
Page 271 and 272: Figure 183: A recent maintenance in
Page 273 and 274: Figure 185: Temperature profiles in
Page 275 and 276: References s point in space Sν(s)
Page 277 and 278: Also the Doppler Centroid anomaly c
Page 279 and 280: Christiansen et al., 2006). The res
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Page 283 and 284: Figure 188: Envisat ASAR wind field
Page 285 and 286: Figure 190: Envisat ASAR wind field
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Satellites in sun-synchronous polar
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500 overlapping scenes for wind res
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(with the fewest samples). The unce
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Badger M., Hasager C. B., Thompson
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Ren Y. Z., Lehner S., Brusch S., Li
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tracking, climate studies, air-sea
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The modified logarithmic wind profi
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sea ice mask was applied and σ0 me
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QuikSCAT 25 20 15 10 5 Horns Rev Fi
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Figure 203:Example of an ASCAT coas
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References Bourassa M.A., Legler D.
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DTU Wind Energy Technical Universit
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