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D. Schlipf, D. J. Schlipf, and M. Kühn, “Nonlinear model predictive control of wind turbines using LIDAR,” Wind Energy, 2012. F. Dunne, D. Schlipf, L. Y. Pao, A. D. Wright, B. Jonkman, N. Kelley, and E. Simley, “Comparison of two independent lidar-based pitch control designs,” in Proc. 50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 2012. D. Schlipf, A. Rettenmeier, M. Hofsäß, M. Courtney, and P. W. Cheng, “Model based wind vector field reconstruction from lidar data,” in Proceedings of the German Wind Energy Conference DEWEK, Bremen, Germany, 2012. J. Jonkman and M. L. Buhl, “FAST user’s guide,” NREL, Tech. Rep. NREL/EL-500-38230, August 2005. B. J. Jonkman and M. L. Buhl, “TurbSim user’s guide,” NREL, Tech. Rep. NREL/TP-500-41136, April 2007. C. L. Bottasso, A. Croce, B. Savini, W. Sirchi, and L. Trainelli, “Aero-servo-elastic modelling and control of wind turbines using finite-element multibody procedures,” Multibody System Dynamics, vol. 16, pp. 291–308, 2006. J. Jonkman, S. Butterfield, W. Musial, and G. Scott, “Definition of a 5-MW reference wind turbine for offshore system development,” Technical Report NREL/TP-500-38060, 2009. E. vander Hooftand T.G.vanEngelen, “Estimated wind speed feed forwardcontrol for windturbine operation optimization,” European Wind Energy Conference, London, vol. 1, p. 9, 2004. D. Schlipf, S. Schuler, P. Grau, F. Allgöwer, and M. Kühn, “Look-ahead cylcic pitch control using LiDAR,” in Proceedings of The Science of making Torque from Wind, Heraklion, Greece, 2010. J. P. Cariou, “Pulsed lidars,” in Remote Sensing for Wind Energy. Risøreport Risø-I-3184(EN). Roskilde, Denmark: A. Peña and C. B. Hasager, June 2011, pp. 65–81, Risø-DTU. A. Rettenmeier, O. Bischoff, M. Hofsäß, D. Schlipf, J. J. Trujillo, and M. Kühn, “Wind field analysis using a nacelle-based lidar system,” in Presentation at the European Wind Energy Conference, Warsaw, Poland, 2010. J. S. Bendat and A. G. Piersol, Random data; analysis and measurement procedures. New York, USA: John Wiley & Sons, 1971. E. Simley and L. Pao, “Correlation between rotating LIDAR measurements and blade effective wind speed,” in 51th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Dallas, USA, 2013. D. Schlipf and P. W. Cheng, “Adaptive Vorsteuerung für Windenergieanlagen,” at - Automatisierungstechnik, vol. 61, pp. 329–338, 2013. D. Schlipf, J. Mann, and P. W. Cheng, “Model of the correlation between lidar systems and wind turbines for lidar assisted control,” submitted to Journal of Atmospheric and Oceanic Technology, 2013. T. Burton, N. Jenkins, D. Sharpe, and E. Bossanyi, Wind Energy Handbook. New York, USA: John Wiley & Sons, 2001. D. Schlipf, P. Fleming, S. Kapp, A. Scholbrock, F. Haizmann, F. Belen, A. Wright, and P. W. Cheng, “Direct speed control using lidar and turbine data,” in Proceedings of the American Control Conference, Washington, USA, 2013. E. Bossanyi, “Un-freezing the turbulence: improved wind field modeling for investigating lidar-assisted wind turbine control,” in Proceedings of the EWEA Annual event, Copenhagen, Denmark, 2012. D. Schlipf, L. Y. Pao, and P. W. Cheng, “Comparison of feedforward and model predictive control of wind turbines using LIDAR,” in Proceedings of the Conference on Decision and Control, Maui, USA, 2012. R. Findeisen, “Nonlinear model predictive control: A sampled-data feedback perspective,” Ph.D. dissertation, Universität Stuttgart, 2005. R. Franke, “Omuses - a tool for the optimization of multistage systems and HQP a solver for sparse nonlinear optimization,” TU Ilmenau, Tech. Rep., 1998. J. Laks, L. Y. Pao, E. Simley, A. Wright, N. Kelley, and B. Jonkman, “Model predictive control using preview measurements from LIDAR,” in Proceedings of the 49th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Orlando, USA, 2011. E. Bossanyi, B. Savini, M. Iribas, M. Hau, B. Fischer, D. Schlipf, T. van Engelen, M. Rossetti, and C. E. Carcangiu, “Advanced controller research for multi-MW wind turbines in the UpWind project,” Wind Energy, vol. 15, no. 1, pp. 119–145, 2012. 190 DTU Wind Energy-E-Report-0029(EN)
E. Hau, Windkraftanlagen, 4th ed. Springer, 2008. A. Kragh, M. Hansen, and T. Mikkelsen, “Improving yaw alignment using spinner based LIDAR,” in Proc. 49th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, 2011. DTU Wind Energy-E-Report-0029(EN) 191
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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
Page 139 and 140: Direct detection of MLH from acoust
Page 141 and 142: Engelbart D.A.M.and Bange J. (2002)
Page 143 and 144: 7 What can remote sensing contribut
Page 145 and 146: 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
Page 149 and 150: 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
Page 153 and 154: KEprofileKEhub 1.2 1.1 1.0 0.9 0.8
Page 155 and 156: PP rated WS Lidarms 1.0 0.8 0.6 0.4
Page 157 and 158: 8 Nacelle-based lidar systems Andre
Page 159 and 160: • Flexibletrajectories.Dependingo
Page 161 and 162: Figure 104: Sketch of simultaneous
Page 163 and 164: The normal wind direction vector nw
Page 165 and 166: Figure 109: Test site at DTU Wind E
Page 167 and 168: Figure 112: Power curve met mast an
Page 169 and 170: Notation C number of sent photons C
Page 171 and 172: 9 Lidars and wind turbine control -
Page 173 and 174: for three unknowns, it is impossibl
Page 175 and 176: model of the blade pitch actuator,
Page 177 and 178: |GRL| [-] 1 0.8 0.6 0.4 0.2 10 k [r
Page 179 and 180: 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
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: fL weighting function GRL transfer
Page 193 and 194: ¨¦¦§©¡§ ¥§¨¦¦§£ ¡¥
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
Page 223 and 224: z [m] 160 100 80 60 40 20 10 15 20
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 and 238: Figure 162: Lavrio: The scatter plo
Page 239 and 240: Figure 163: Panahaiko: The scatter
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References Albers A. and Janssen A.
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Wexler (1968), where the limitation
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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
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involves interaction of all compone
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Lindelöw P. (2007) Fibre Based Coh
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plications, including meteorologica
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Figure 173: Rayleigh-Jeans’ appro
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Figure 176: Brightness temperature
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with rain are shown in Fig. 179. A
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This method gives a good accuracy (
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Figure 183: A recent maintenance in
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Figure 185: Temperature profiles in
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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
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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
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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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