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Figure 69: Comparison of Windcube 10-min averages with calibrated mast at Risø Høvsøre 5.4 Conclusions and perspectives This chapter summarizes the principles of operation, the performances and critical parameters of Doppler pulsed wind lidars, such as the WINDCUBE lidar from LEOSPHERE. It takes into accountthe different steps ofthe velocitymeasurement process.Thisdetailedprocess analysis provides the backbone for a pulsed lidar development, optimization and issues related to serial production. More generally, pulsed and CW lidars have shown great reliability and accuracy in the measurement of wind characteristics such as horizontal and vertical wind speed and wind direction at various heights on flat terrains and offshore (Courtney et al., 2009; Westerhellweg et al., 2010).Thehigh datarecovery rate up toabove bladetop ensuresahighqualityanalysis of the wind conditions available on the project site, leading to an optimized layout design and the choice of the suitable turbines. Regarding future improvements, the measurement of additional wind parameters like turbulence intensities, kinetic fluxes or inflow-angles is under investigation at the present time and should be available in the near future. One of the remaining challenges is also reaching high accuracy in complex terrains where flow distortion occurs and impairs the lidar wind components retrieval from measured radial velocities. Since lidar radial velocities are still very accurate even on complex and rough terrains, a methodology using CFD modeling has been recently developed to avoid taking the flow homogeneity assumption (Boquet et al., 2009; Bingöl et al., 2009; Boquet et al., 2010a).Eventhoughnew, thismethodologyhas alreadyshowngoodresultsonsitesofvarious complexities. The measurement of additional wind parameters like turbulence intensities, kineticfluxesorinflowanglesisunderinvestigationatthepresenttimeandshouldbe available in the near future. To sum up, lidar anemometry has already proven its great utility in the development of wind farm projects, as an instrument allowing considerable financial gains through a better understanding of the wind conditions at a site and therefore reducing the capital risk of the investors (Boquet et al., 2010b), but also for operational power curves measurements 118 <strong>DTU</strong> Wind Energy-E-Report-0029(EN)
(Gottshall et al., 2010; Albers et al., 2010). With the general trend to develop larger wind turbines and wider wind farms, the lidar technology is elected to be more and more widely used. <strong>DTU</strong> Wind Energy-E-Report-0029(EN) 119
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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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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: 5.3.6 Existing systems and actual p
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
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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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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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