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the cases where there is no difference between the lidar and the mast measurements. The black line is the model and the points are the measurement results. 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. We believe this can be a result of the limitation of WAsP Engineering. In southerly directions very close to the site there are steep slopes. In this sector and height, the flow model has difficulties predicting the tilt angles as compared to sonic measurements for periods with no icing problems. 12.4.2 Mountainous site; Panahaiko The Panahaiko site is located 165 km northwest of Athens, at Vounogiorgis mountain south east of the village Sella, 14 km south of the Patras Sea. The experiment ran from 2007-Sep- 19 to 2007-Oct-11. The terrain in the vicinity of the site is very complex. Highest point is 2000mintheregionwheretheexperimentsurroundingisbetween1700and1750mASL. The prevailing wind directions are ENE and SW. The triangular lattice reference meteorological mast has six cup anemometers (10, 20, 30, 40, and 54 m) and two vanes (40 and 54 m). Additionally, there are also air temperature and relative humidity measurements at 54 m. The boom cross-section is 40 mm×40 mm. All wind sensors are mounted at a height of 75 cm above the boom and at a distance of 225 cm from the outer mast leg. The lidar is located nearly 20 m WSW of the mast. The lidar measurement heights are 30 m and 55 m. The second site, Panahaiko, is much more complex than Lavrio, so there are many sectors which could be problematic for WAsP Engineering to model. The scatter plots in Figure 163 (top) show data for all directions. The mast at Panahaiko is smaller than at Lavrio so the sector with flow distortion is smaller (±25 ◦ ) shown in grey in the figure. The boom direction is 210 ◦ . Thecomparisonbetweenthemodellederrorandthemeasurementsasafunctionofdirection is shown in Figure 163 (lower two plots). It is not a perfect prediction, but the model gives the right order of magnitude for this complex site. The outliers mainly seen for the larger heights in Figures 162 and 163 are probably due to cloud return as discussed in Courtney et al. (2008). 12.5 Conclusions Lidars, used over flat homogeneous terrain, show errors in the mean wind speed of only a few percent. We have shown that in complex terrain of the type commonly used for wind turbine parks, errors in the horizontal wind speed as measured by a conically scanning lidar can be of the order of 10%. This is due to the lack of horizontal homogeneity of the flow, which is assumed in the interpretation of the lidar data. The findings are based on two experiments involving collocated lidars and meteorological masts in complex terrain, together with flow calculations over the same terrains. For that calculation we use WAsP Engineering, and we find that the calculations match the experiment except for some sectors where the terrain is particularly steep. This is not surprising, since the WAsP Engineering is built on a linearized flow model, which is only valid for limited terrain slopes.The model is not for highly complex terrain that can incorporate the stability effect in any reliable way. Furthermore most of the wind speeds analyzed, from both sites, are quite high so it is not unreasonable to assume neutral stratification. That is why there is concluding thoughts about stability. To make more reliablepredictionsoftheerrorinverysteepterrain,othermoreadvancedflowmodels((Castro et al., 2003)) must be used. The hypothesis that the lidar conical scan error due to inhomogeneity of the mean flow is independent of the half opening angle φ on the horizontal components has been supported with experimental results from moderately-complex terrain site measurements. The synchronized measurements from the lidars with different half opening angles and meteorological mast instruments reported no positive effect of smaller half opening angle in horizontal wind speed measurements, contrary to what was being suggested by some of the producers and 238 <strong>DTU</strong> Wind Energy-E-Report-0029(EN)
Figure 163: Panahaiko: The scatter plots show generally 4% to 7% errors in wind speed measurements (top). Lower two plots are the comparison between the model and the measurement data for two different heights. Small red dots are the error ratio for each 10 min measurement, big blue dots are the averaged 10 ◦ 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. It is not a perfect prediction, but the model gives the right order of magnitude for this complex site. <strong>DTU</strong> Wind Energy-E-Report-0029(EN) 239
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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
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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
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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 and 236: Uconst wΑx l h Φ h.tanΦ Figure 1
Page 237: Figure 162: Lavrio: The scatter plo
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)
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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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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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