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Figure 195: Mean wind speed map based on 104 Envisat ASAR wind fields at Shirahama, Japan. Courtesy: Yuko Takeyama. Figure 196: Maps of the 10-m mean wind speed (left) and energy density (right) from SAR over Hangzhou Bay in China. Very high values (bright red) near the coastline are caused by a high radar return from exposed sand or mud and are not associated with the wind. From Badger (2009). 290 <strong>DTU</strong> Wind Energy-E-Report-0029(EN)
(with the fewest samples). The uncertainty of Weibull k is around 0.04 and for energy density from 20 to 50 Wm −2 . 15.13 Lifting satellite winds to hub-height The SAR-based wind results are valid at 10 m above sea level (modern turbines operate at 100 m). The vertical wind profile offshore is a function of atmospheric stability, roughness and boundary layer height (Peña et al. 2012). A method to extrapolate the satellite wind resource statistics to wind turbine hub-height is developed. It is based on combining atmospheric stabilityinformationfrom mesoscalemodelsintothestabilitydependentwindprofileequation. Preliminary results are presented (Hasager at al., 2012). The potential use of the lifting of satellite winds to hub-height is applicable for all types of 10 m satellite wind resource statistics including SAR and scatterometer. An improvement to the method is currently being investigated. The key difference is the use of stability information per scene versus average stability. It is expected to be more robust and reliable to use average stability for lifting winds to hub-height. 15.14 Future advances in ocean wind mapping from SAR For end-users the level 2 wind product from Sentinel-1 is foreseen to be important new data. There will still be the need for evaluation and improvement on SAR wind retrieval. Investigations of Doppler shift anomaly in combination with GMF is one way as well as new adjustment to polarization ratio and validated GMFs for X- and L-band. Re-processing of the full Envisat archive to wind fields would be relevant for European scale wind resource mapping such as the New European Wind Atlas. 15.15 Acknowledgements We are very thankful for collaboration with Frank Monaldo and Alexis Mouche. Envisat data provided by the European Space Agency and RADARSAT-1 data from MacDonald, Dettwiler and Associates Ltd are acknowledged. Projects we acknowledge are Offshore wind mapping in India with Centre for Wind Energy Technology (C-WET), FP7 NORSEWInD TREN-FP7-219048, FP7 EERA DTOC FP7-ENERGY-2011-1/ n ◦ 282797, ICEWIND Nordic Top-levelResearchInitiative,FP7 EuropeanRegionalDevelopmentFundandtheSouthBaltic Programme: South Baltic Offshore Wind Energy Regions project, Off-Shore Wind Energy Resource Assessment and Feasibility Study of Off-Shore Wind Farm Development in China (EU-China Energy and Environment Programme), Offshore wind energy analyses over the United Arab Emirates is funded by Abu Dhabi Future Energy Company (Masdar). We enjoy the long-term visit of Yuko Takeyama. Notation A function of wind speed and local incident angle in a GMF A Weibull scale parameter ALOS Advanced Land Observing Satellite (Japan) ANSWRS APL/NOAA SAR wind retrieval system ASAR advanced C-band SAR ASCAT Advanced Scatterometer ASI Italian Space Agency B function of wind speed and local incident angle in a GMF C function of wind speed and local incident angle in a GMF CAST China Association for Science and Technology CSA Canadian Space Agency <strong>DTU</strong> Wind Energy-E-Report-0029(EN) 291
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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
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Pel/Pel,max [-] 1 0.98 0.96 0.94 0.
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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
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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
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Uconst wΑx l h Φ h.tanΦ Figure 1
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Figure 162: Lavrio: The scatter plo
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Page 255 and 256: RMSPE (%) 70 60 50 40 30 20 10 0 vu
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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
Page 287 and 288: Satellites in sun-synchronous polar
Page 289: 500 overlapping scenes for wind res
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Page 295 and 296: Ren Y. Z., Lehner S., Brusch S., Li
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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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