References Wb(r) blade effective weighting function W(Fℓ,Rℓ) lidar range weighting function wm wind speed disturbance preview measurement wt wind disturbance at the turbine x longitudinal (along-wind) direction y transverse (horizontal) direction; output error variable from the turbine y0 collective component of output error variable from the turbine yh horizontal component of output error variable from the turbine yv vertical component of output error variable from the turbine z vertical direction γ2 ab (f) ∆h ˆ∆h coherence between signals a and b horizontal shear wind speed component estimate of horizontal shear component from lidar measurements ∆v vertical shear wind speed component ˆ∆v estimate of vertical shear component from lidar measurements θR rotor position ψ azimuth angle in rotor plane Ωg generator speed ΩR rotor speed Bir, B. (2008) Multiblade coordinate transformation and its application to wind turbine analysis. Proc. AIAA Aerospace Sciences Meeting, Reno, NV Bossanyi, E. A. (2005) Further load reductions with individual pitch control. Wind Energy 8:481–485 Bossanyi, E. A. (2012) Un-freezing the turbulence: Improved wind field modeling for investigating lidar-assisted wind turbine control. Proc. European Wind Energy Association Annual Event (EWEA), Copenhagen, Denmark Bossanyi, E. A., Kumar, A., and Hugues-Salas, O. (2012b) Wind turbine control applications of turbinemounted lidar. Proc. Science of Making Torque from Wind, Oldenburg, Germany Dunne, F., Pao, L. Y., Wright, A. D., Jonkman, B., Kelley, N., and Simley, E. (2011a) Adding feedforward blade pitch control for load mitigation in wind turbines: Non-causal series expansion, preview control, and optimized FIR filter methods. Proc. AIAA Aerospace Sciences Meeting, Orlando, FL Dunne, F., Pao, L. Y., Wright, A. D., Jonkman, B., and Kelley, N. (2011b) Adding feedforward blade pitch control to standard feedback controllers for load mitigation in wind turbines. Mechatronics 21:682–690 Dunne, F., Schlipf, D., Pao, L. Y., Wright, A. D., Jonkman, B., Kelley, N., and Simley, E. (2012) Comparison of two independent LIDAR-based pitch control designs. Proc. AIAA Aerospace Sciences Meeting, Nashville, TN Franklin, G. F., Powell, J. D., and Emami-Naeini,A. (2006) Feedback control of dynamic systems, fifth edition, Pearson Prentice Hall Frehlich, R. and Kavaya, M. (1991) Coherent laser performance for general atmospheric refractive turbulence. Applied Optics 30:5325–5352 Frehlich, R., Meillier, Y., Jensen, M., Balsley, B. and Sharman, M. (2006) Measurements of boundary layer profiles in an urban environment. Applied Meteorology and Climatology 45:821–837 Harris, M., Hand, M., and Wright, A. (2005) Lidar for turbine control, NREL/TP-500-39154, Tech. Rep., National Renewable Energy Laboratory, Golden, CO Harris, M., Bryce, D., Coffey, A., Smith, D., Birkemeyer, J., and Knopf, U. (2007) Advance measurements of gusts by laser anemometry. Wind Engineering and Industrial Aerodynamics 95:1637–1647 Hazell, A. (2008) Discrete-Time Optimal Preview Control, Ph.D. Thesis, Imperial College, University of London, London, England IEC (2001) IEC 61400-13 Ed. 1: Wind turbine generator systems: Measurement of mechanical loads. Int. Electrotechnical Commission IEC (2005) IEC 61400-1 Ed. 3: Wind turbines - Part 1: Design requirements. Int. Electrotechnical Commission Jonkman, J. and Buhl, M. (2005) FAST user’s guide, NREL/TP-500-38230, Tech. Rep., National Renewable Energy Laboratory, Golden, CO 218 <strong>DTU</strong> Wind Energy-E-Report-0029(EN)
Jonkman, B. (2009) TurbSim user’s guide: Version 1.50, NREL/TP-500-46198, Tech. Rep., National Renewable Energy Laboratory, Golden, CO Jonkman, J., Butterfield, S., Musial, W., and Scott, G. (2009) Definition of a 5-MW reference wind turbine for offshore system development, NREL/TP-500-38060, Tech. Rep., National Renewable Energy Laboratory, Golden, CO Kodama, N., Matsuzaka, T., Tuchiya, K., and Arinaga, S. (1999) Power variation control of a wind generator by using feed-forward control. Renewable Energy 16:847–850 Kragh, K. A., Hansen, M. H., and Mikkelsen, T. (2013) Precision and shortcomings of yaw error estimation using spinner-based light detection and ranging. Wind Energy 16:353–366 Kristensen, L. (1979) On longitudinal spectral coherence. Bound.-Layer Meteorol. 16:145–153 Laks, J., Pao, L. Y., Simley, E., Wright, A. D., Kelley, N., and Jonkman, B. (2011a) Model predictive control using preview measurements from lidar. Proc. AIAA Aerospace Sciences Meeting, Orlando, FL Laks,J.,Pao,L.Y.,Wright,A.D.,Kelley,N.,andJonkman, B.(2011b) The use ofpreview windmeasurements for blade pitch control. Mechatronics 21:668–681 Laks, J., Simley, E., and Pao, L. Y. (2013) A spectral model for evaluating the effect of wind evolution on wind turbine preview control. Proc. American Control Conference, Washington, D.C. Mikkelsen, T. (2009) On mean wind and turbulence profile measurements from ground-based wind lidars: limitations in time and space resolution with continuous wave and pulsed lidar systems. Proc. European Wind Energy Conference, Stockholm, Sweden Mikkelsen, T., Angelou, N., Hansen, K., Sjöholm, M., Harris, M., Slinger, C., Hadley, P., Scullion, R., Ellis, G., and Vives, G. (2013) A spinner-integrated wind lidar for enhanced wind turbine control. Wind Energy 16:625–643 Mirzaei, M., Soltani, M., Poulsen, N. K., and Niemann, H. H. (2013) Model predictive control of wind turbines using uncertain lidar measurements. Proc. American Control Conference, Washington, D.C. Moriarty, P.J. and Hansen, A.C. (2005) Aerodyn Theory Manual, NREL/TP-500-36881, Tech. Rep., National Renewable Energy Laboratory, Golden, CO Pao, L. Y. and Johnson, K. E. (2011) Control of wind turbines: Approaches, challenges, and recent developments. IEEE Control Systems Magazine 31:44–62 Pedersen, A. T., Sjöholm, M., Angelou, N., Mikkelsen, T., Montes, B. F., Pedersen, J. E., Slinger, C., and Harris, M. (2013) Full-scale field test of a blade-integrated dual-telescope wind lidar. Proc. European Wind Energy Association Annual Event (EWEA), Vienna, Austria Rettenmeier, A., Bischoff, O., Hofsäß, M., Schlipf, D., and Trujillo, J. J. (2010) Wind field analysis using a nacelle-based LIDAR system. Proc. European Wind Energy Conference, Warsaw, Poland Schlipf, D. and Kühn, M. (2008) Prospects of a collective pitch control by means of predictive disturbance compensation assisted by wind speed measurements. Proc. German Wind Energy Conference (DEWEK), Bremen, Germany Schlipf, D., Trujillo, J. J., Basterra, V., and Kühn, M. (2009) Development of a wind turbine lidar simulator. Proc. European Wind Energy Conference, Marseille, France Schlipf, D., Schuler, S., Grau, P., Allgöwer, F., and Kühn, M. (2010) Look-ahead cyclic pitch control using LIDAR. Proc. Science of Making Torque from Wind, Heraklion, Greece Schlipf, D., Kapp, S., Anger, J., Bischoff, O., Hofsäß, M., Rettenmeier, A., Smolka, U., and Kühn, M. (2011) Prospects of optimization of energy production by lidar assisted control of wind turbines. Proc. European Wind Energy Association Annual Event (EWEA), Brussels, Belgium Schlipf, D., Mann, J., Rettenmeier, A., and Cheng, P. W. (2012a) Model of the correlation between lidar systems and wind turbines for lidar assisted control. Proc. Int. Symp. for the Advancement of Boundary Layer Remote Sensing, Boulder, CO Schlipf, D., Schlipf, D. J., and Kühn, M. (2012b) Nonlinear model predictive control of wind turbines using LIDAR. Wind Energy Scholbrock, A. K., Fleming, P . A., Fingersh, L. J., Wright, A. D., Schlipf, D., Haizman, F., and Belen, F. (2013) Field testing LIDAR based feed-forward controls on the NREL controls advanced research turbine. Proc. AIAA Aerospace Sciences Meeting, Grapevine, TX <strong>DTU</strong> Wind Energy-E-Report-0029(EN) 219
- 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 and 8:
8 Nacelle-based lidar systems 157 8
- Page 9 and 10:
12 Complex terrain and lidars 231 1
- 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
- Page 63 and 64:
and fSw(f) u 2 ∗ = 1.05n 1+5.3n 5
- Page 65 and 66:
and n = 0.468. This spectrum implie
- Page 67 and 68:
to be calculated. We do that on a m
- Page 69 and 70:
Notation A Charnock constant neutra
- Page 71 and 72:
Maxey M. R. (1982) Distortion of tu
- Page 73 and 74:
4.2 Basic principles of lidar opera
- Page 75 and 76:
4.2.5 Wind profiling in conical sca
- Page 77 and 78:
4.3.1 Behaviour of scattering parti
- Page 79 and 80:
the beam radius at the output lens.
- Page 81 and 82:
from which the value of VLOS is der
- Page 83 and 84:
the atmosphere. The SNR 4 for a win
- Page 85 and 86:
individual line-of-sight wind speed
- Page 87 and 88:
A general approach to mitigating th
- Page 89 and 90:
from ±VH sinδ (if the tilt is tow
- Page 91 and 92:
as a down draught (of the same abso
- Page 93 and 94:
Table 7: Combined results from 28 Z
- Page 95 and 96:
in Eastern Jutland between January
- Page 97 and 98:
Figure 56: Normalized power curves
- Page 99 and 100:
the concept. Developments include i
- Page 101 and 102:
References x horizontal position in
- Page 103 and 104:
Wagner R., Mikkelsen T., and Courtn
- Page 105 and 106:
5.2 End-to-end description of pulse
- Page 107 and 108:
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
- Page 113 and 114:
This wavelength is also the most fa
- Page 115 and 116:
Eq.(132)isadaptedforcollimatedsyste
- Page 117 and 118:
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.
- Page 123 and 124:
derived from fluctuations of the wi
- Page 125 and 126:
Acoustic received echo (ARE) method
- Page 127 and 128:
Figure 71: Sample time-height cross
- Page 129 and 130:
A gradient minimum is characterized
- Page 131 and 132:
Figure73:Bragg-relatedacoustic(belo
- Page 133 and 134:
stability (inversion strength) can
- Page 135 and 136:
Figure 76: Combined soundingwith a
- Page 137 and 138:
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 and 190: fL weighting function GRL transfer
- Page 191 and 192: E. Hau, Windkraftanlagen, 4th ed. S
- 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: • Measurement coherence, which ca
- 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
- 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
- Page 247 and 248: where x is the center of the scanni
- 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
- Page 281 and 282:
educe speckle noise, a random noise
- Page 283 and 284:
Figure 188: Envisat ASAR wind field
- Page 285 and 286:
Figure 190: Envisat ASAR wind field
- Page 287 and 288:
Satellites in sun-synchronous polar
- Page 289 and 290:
500 overlapping scenes for wind res
- Page 291 and 292:
(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
- Page 301 and 302:
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