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9.9.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 9.10 Conclusion and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 10 Lidars and wind turbine control – Part 2 192 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 10.1.1 Lidar Measurement Strategies . . . . . . . . . . . . . . . . . . . . . 194 10.2 Wind Turbine Feedforward Control . . . . . . . . . . . . . . . . . . . . . . . 195 10.2.1 Feedforward Control Assuming Perfect Measurements . . . . . . . . . 195 10.2.2 Feedforward Control with Imperfect Measurements . . . . . . . . . . 197 10.2.3 The Relationship between Variance and Damage Equivalent Loads . . 198 10.3 Preview Time in Feedforward Control . . . . . . . . . . . . . . . . . . . . . . 198 10.3.1 Ideal Feedforward Controller Preview Time . . . . . . . . . . . . . . . 198 10.3.2 Prefilter Preview Time . . . . . . . . . . . . . . . . . . . . . . . . . 199 10.3.3 Pitch Actuation Preview Time . . . . . . . . . . . . . . . . . . . . . 199 10.4 Blade Effective Wind Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 10.4.1 The Relative Importance of the u, v, and w Components . . . . . . . 201 10.5 Lidar Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 10.5.1 Range Weighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 10.5.2 Geometry Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 10.5.3 Wind Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 10.5.4 Lidar Measurements of Blade Effective Wind Speed . . . . . . . . . . 206 10.6 Lidar Measurement Example: Hub Height and Shear Components . . . . . . 207 10.6.1 Optimizing the Measurement Scenario . . . . . . . . . . . . . . . . . 209 10.7 ControlExample1:WindTurbinePreviewControlInThePresenceofEvolving Turbulence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 10.7.1 H2 Optimal Preview Control . . . . . . . . . . . . . . . . . . . . . . 212 10.7.2 Controller Performance Simulations . . . . . . . . . . . . . . . . . . 213 10.7.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 10.8 Control Example 2: H2 Optimal Control with Model of Measurement Coherence215 10.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 11 Lidars and wind profiles 221 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 11.2 Wind profile theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 11.2.1 Surface layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 11.2.2 Marine surface layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 11.2.3 Boundary layer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224 11.3 Comparison with observations at great heights . . . . . . . . . . . . . . . . . 224 11.3.1 Marine observations . . . . . . . . . . . . . . . . . . . . . . . . . . . 224 11.3.2 Neutral observations over flat land . . . . . . . . . . . . . . . . . . . 225 11.3.3 Diabatic observations over flat land . . . . . . . . . . . . . . . . . . 226 11.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 8 DTU Wind Energy-E-Report-0029(EN)
12 Complex terrain and lidars 231 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 12.2 Lidars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 12.2.1 ZephIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232 12.2.2 Windcube . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 12.3 Challenges and Known Issues . . . . . . . . . . . . . . . . . . . . . . . . . . 234 12.3.1 The conical scanning error in complex terrain . . . . . . . . . . . . . 234 12.3.2 Predicting the error by means of a flow model . . . . . . . . . . . . . 235 12.4 Experimental studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 12.4.1 Hilly site; Lavrio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 12.4.2 Mountainous site; Panahaiko . . . . . . . . . . . . . . . . . . . . . . 238 12.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 13 Turbulence measurements by wind lidars 242 13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 13.2 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 13.2.1 Systematic turbulence errors for the ZephIR lidar . . . . . . . . . . . 245 13.2.2 Systematic turbulence errors for the WindCube lidar . . . . . . . . . 247 13.2.3 Definition of the systematic error . . . . . . . . . . . . . . . . . . . . 248 13.3 Comparison of models with the measurements . . . . . . . . . . . . . . . . . 249 13.3.1 Comparison with the ZephIR measurements . . . . . . . . . . . . . . 249 13.3.2 Comparison with the WindCube measurements . . . . . . . . . . . . 252 13.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 13.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 13.6 Future Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258 14 Ground based passive microwave radiometry and temperature profiles 260 14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 14.2 Microwave radiometry fundamentals . . . . . . . . . . . . . . . . . . . . . . 260 14.3 Upward-looking radiometric temperature profile measurements . . . . . . . . 261 14.4 Upward-looking angular scanning microwave radiometry . . . . . . . . . . . . 265 14.5 An angular scanning temperature profile radiometer . . . . . . . . . . . . . . 269 14.6 Antarctica Dome C experimental site Radiometric measurements . . . . . . . 270 14.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 15 SAR for wind energy 276 15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 15.2 SAR technical description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 15.3 Wind retrieval from SAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278 15.4 Beyond C-band VV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 15.5 Current practices in SAR wind retrieval . . . . . . . . . . . . . . . . . . . . . 280 15.6 SAR wind retrieval at DTU Wind Energy. . . . . . . . . . . . . . . . . . . . 282 15.7 Mesoscale wind phenomena from SAR . . . . . . . . . . . . . . . . . . . . . 282 15.8 SAR wind fields near offshore wind farms . . . . . . . . . . . . . . . . . . . . 284 15.9 Wind resources from SAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 15.10S-WAsP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 15.11The wind class method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288 15.12SAR wind resource maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 DTU Wind Energy-E-Report-0029(EN) 9
Page 1 and 2: Remote Sensing for Wind Energy DTU
Page 3 and 4: Author: Alfredo Peña, Charlotte B.
Page 5 and 6: 4 Introduction to continuous-wave D
Page 7: 8 Nacelle-based lidar systems 157 8
Page 11 and 12: 1 Remote sensing of wind Torben Mik
Page 13 and 14: Figure 2: Calibration, laboratory w
Page 15 and 16: Figure 3: Example of scatter plots
Page 17 and 18: 1.2.3 Summary of sodars Most of tod
Page 19 and 20: 1.3.3 Wind lidars Measuring wind wi
Page 21 and 22: Figure 6: CW wind lidars (ZephIRs)
Page 23 and 24: Further developments Furthermore, n
Page 25 and 26: 2 The atmospheric boundary layer S
Page 27 and 28: Figure 9: Large spatial scale varia
Page 29 and 30: Du3 Dt Du1 Dt Du2 Dt The three mome
Page 31 and 32: Figure 13: Consensus relations betw
Page 33 and 34: ψ z L ∼ − 5 L . For unstable c
Page 35 and 36: Figure 15: Behavior of the turbulen
Page 37 and 38: Figure 17: Newly developed models t
Page 39 and 40: The value of q0 at the surface is d
Page 41 and 42: the spray is the source of icing on
Page 43 and 44: u∗2 u∗1 u1(h) = u∗1 k ln h
Page 45 and 46: Figure 26:Land-seabreeze system,whe
Page 47 and 48: Figure28:Three dimensionalpicture o
Page 49 and 50: sufficient information. Finally, we
Page 51 and 52: Mann, J. (1998) Wind field simulati
Page 53 and 54: (2010) and comparison under differe
Page 55 and 56: To get the velocity field from the
Page 57 and 58: τ(k) [Arbitrary units] 10 3 10 2 1
Page 59 and 60:
(Koopmans, 1974; Bendat and Piersol
Page 61 and 62:
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
Page 71 and 72:
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
Page 101 and 102:
References x horizontal position in
Page 103 and 104:
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
Page 109 and 110:
Figure 62: Radial wind velocity ret
Page 111 and 112:
transform in order to use data obta
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This wavelength is also the most fa
Page 115 and 116:
Eq.(132)isadaptedforcollimatedsyste
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5.3.6 Existing systems and actual p
Page 119 and 120:
(Gottshall et al., 2010; Albers et
Page 121 and 122:
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
Page 139 and 140:
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
Page 147 and 148:
Bottom of rotor Φ rotation r w u
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Height Height Hub 1.6 1.4 1.2 1.0 0
Page 151 and 152:
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) [
Page 181 and 182:
0.04 0.03 ˆk [ rad m ] 0.02 0.63 0
Page 183 and 184:
[%] 10 0 −10 −20 −30 MyT Moop
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PSD(θ1) [rad 2 /Hz] PSD(Moop1) [Nm
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Pel/Pel,max [-] 1 0.98 0.96 0.94 0.
Page 189 and 190:
fL weighting function GRL transfer
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E. Hau, Windkraftanlagen, 4th ed. S
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¨¦¦§©¡§ ¥§¨¦¦§£ ¡¥
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vertical (m) 150 100 50 R d 0
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With feedback only, on the other ha
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Figure 136: Estimated preview requi
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Normalized C r = C r P Q 2 W (r) b
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Normalized C r = C r P Q 2 W (r) b
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Coherence 1 0.8 0.6 0.4 0.2 0 10
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Coherence 1 0.8 0.6 0.4 0.2 0 10
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Magnitude Squared 10 8 10 7 10 6 10
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Figure 148: During simulation, FAST
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Figure 149: Collective flap respons
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Magnitude (abs) blade pitch gen spe
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• Measurement coherence, which ca
Page 219 and 220:
Jonkman, B. (2009) TurbSim user’s
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11 Lidars and wind profiles Alfredo
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z [m] 160 100 80 60 40 20 10 15 20
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z [−] zo 1 κ ln 40 38 36 34 32 3
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z [m] z [m] 1000 900 800 700 600 50
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the growth of the length scale, agr
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12 Complex terrain and lidars Ferha
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Figure 158: The ZephIR models which
Page 235 and 236:
Uconst wΑx l h Φ h.tanΦ Figure 1
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Figure 162: Lavrio: The scatter plo
Page 239 and 240:
Figure 163: Panahaiko: The scatter
Page 241 and 242:
References Albers A. and Janssen A.
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Wexler (1968), where the limitation
Page 245 and 246:
13.2.1 Systematic turbulence errors
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where x is the center of the scanni
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The theoretical systematic errors a
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Height (m) 160 140 120 100 80 60 40
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Height (m) Height (m) 160 140 120 1
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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
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plications, including meteorologica
Page 263 and 264:
Figure 173: Rayleigh-Jeans’ appro
Page 265 and 266:
Figure 176: Brightness temperature
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with rain are shown in Fig. 179. A
Page 269 and 270:
This method gives a good accuracy (
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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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Also the Doppler Centroid anomaly c
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Christiansen et al., 2006). The res
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educe speckle noise, a random noise
Page 283 and 284:
Figure 188: Envisat ASAR wind field
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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
Page 293 and 294:
Badger M., Hasager C. B., Thompson
Page 295 and 296:
Ren Y. Z., Lehner S., Brusch S., Li
Page 297 and 298:
tracking, climate studies, air-sea
Page 299 and 300:
The modified logarithmic wind profi
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sea ice mask was applied and σ0 me
Page 303 and 304:
QuikSCAT 25 20 15 10 5 Horns Rev Fi
Page 305 and 306:
Figure 203:Example of an ASCAT coas
Page 307 and 308:
References Bourassa M.A., Legler D.
Page 309:
DTU Wind Energy Technical Universit
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