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Notation B detector bandwidth c speed of light Cn index structure constant CNR carrier-to-noise ratio CRLB Cramer Rao lower bound CW continuous wave do coherence diameter of the beam DBS Doppler beam swinging FFT fast Fourier transform Fi intermediate frequency Fs sampling frequency Ft instrumental geometrical focus distance (wave curvature radius at telescope) FWHM full width half maximum < i2 NEP > I(Z) shot noise Lorentzian function IPP inter pulse period LMA large mode area LOS line of sight LO local oscillator MLE maximum likelihood estimator MO master oscillator MOPA master oscillator power amplifier NEP noise equivalent power density PLO local oscillator power Ppeak transmitted pulse peak power Pr total optical power PRF pulse repetition frequency PRFmax maximum pulse repetition frequency N number of average pulses R focus distance RWF(Z) range weighting function S detector sensitivity SNR signal to noise ratio Tatm atmospheric round trip transmission Tinst instrumental round trip transmission u north-south wind speed component v east-west wind speed component VAD velocity azimuth display Vd(Z) velocity measurement at one point Vri LOS velocities, i = N North, i = E East, i = S South, i = W West and i = Z Zenith w vertical wind speed component Z height Zmax maximum height to be measured Zo distance where I(Z) is maximum, i.e. signal to noise is maximum Zr Rayleigh distance β backscattering atmospheric coefficient ∆Z half of FWHM geometric depth of focus ηfoc focusing efficiency ηhet heterodyne efficiency θ lidar cone angle λ laser wavelength ν optical frequency σ efficient telescope aperture radius σv global velocity resolution σvcrlb Cramer Rao lower bound of velocity resolution σvsat smallest value of the velocity resolution τ pulse duration τc finite correlation time τm measurement time 120 DTU Wind Energy-E-Report-0029(EN)
References Albers A., Janssen A. W. and Mander J. (2010) Hot to gain acceptance for lidar measurements. DEWEK Ando T., Kameyama S. and Hirano Y. (2008) All-fiber coherent Doppler lidar technologies at Mitsubishi Electric Corporation. IOP Conf. Series: Earth and Environ. Science 1:012011 Banakh V. and Smalikho I. (1994) Estimation of the turbulence energy dissipation rate from the pulsed Doppler lidar data. J. Atmos. Oceanic Technol. 10 Bingöl F., Mann J. and Foussekis D. (2009) Conically scanning lidar error in complex terrian. Meteorol. Z. 18:1–7 Boquet M.,Parmentier R.and Cariou J. P. (2009) Analysis and optimization of pulsed Doppler lidar incomplex terrain. EWEC, Marseille Boquet M., Parmentier R. and Cariou J. P. (2010) Correction of pulsed wind lidars bias in complex terrain. ISARS Boquet M., Callard P., Neve N. and Osler E. G. (2010) Return on investment of a lidar remote sensing device. DEWI Magazin 37:56–61 Cariou J.-P., Augere B. and Valla M. (2006) Laser source requirements for coherent lidars based on fiber technology. Comptes Rendus Physique 7:213–223 Courtney M., Wagner R. and Lindelöw P. (2009) Commercial lidar profilers for wind energy. A comparative guide. EWEC, Marseille Faghani D. et al. (2009) Remote sensing: practical use for wind power project. AWEA Wind Resource & Project Energy Assessment Workshop, Portland Frehlich R. G. and Kavaya M. J. (1991) Coherent laser radar performance for general atmospheric refractive turbulence. Appl. Opt. 30 Frehlich R. G., Hannon S. M., Henderson S. W. (1993) Performance of a 2-coherent Doppler lidar for wind measurements. J. Atmos. Oceanic. Technol. 11:1517– Frehlich R. G., Hannon S. M. and Henderson S. W. (1998) Coherent Doppler lidar measurements of wind field statistics. Bound.-Layer Meteorol. 86:233–256 Gottshall J.,Courtney M.S.,Lindelöw P.andAlbers A.(2010) Classificationoflidar profilers: Howto introduce lidars to power performance testing. EWEC, Brussels Huffaker R. M. and Hardestry R. M. (1996) Remote sensing of Atmospheric wind velocities using solid state and CO2 coherent laser systems. Proc. IEEE 84 Jaynes D. (2009) Lidar validation and recommendations for wind resource assessments. Wind Power Conf., Chicago Lindelöw P. (2007) Effective sample volume fiber based coherent lidars for remote sensing of wind. PhD thesis from Ørsted-DTU, Lyngby Mikkelsen T.(2008) WindScanner: a facilityforwindand turbulence measurements around large windturbines. In Nordic Conf. on: Global Challenges-Regional Opportunities: How can research infrastracture and Science support Nordic competiveness?, Stockholm Oldroyd A. et al. (2009) Testing and calibration of various lidar remote sensing devices for a 2 year offshore measurement campaign. EWEC, Marseille Siegman A. E. (1966) The antenna properties of optical heterodyne receivers. Appl. Optics 5:1588–1594 Soreide D., Boque R. K., Seidel J. and Ehernberger L. J. (1997) The use of a lidar forward looking turbulence sensor for mixed-compression inlet unstart avoidance and gross weight reduction on a high speed civil transport Nasa Technical memorandum Westerhellweg A., Beeken A., Canadillas B., Neumann T. (2010) One year of lidar measurements at Fino1platform: comparison and verification to met-mast data. DEWEK DTU Wind Energy-E-Report-0029(EN) 121
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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
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
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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: (Gottshall et al., 2010; Albers et
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
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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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