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7. A recent study by Rogers et al. (2012) analysed a variety of scenarios that could be addressed by turbine mounted lidar, including retrofitting lidar to existing turbines, larger rotors and taller towers. Benefits of turbine mounted lidar included a 6 year life extension and 30% increase in total energy production (when a lidar was retrofitted to a 2.5 MW turbine); an increase in permitted rotor area of 6% and an associated energy output increase of 4% (larger rotor on 5 MW turbine); a 3% energy output increase from a greater allowable tower height, achieved through reduced fatigue loads (again on a 5 MW turbine). The same study also estimated an achievable increase in energy output due to optimisation of lidar control alone to be just 0.6%. Clearly, turbine mounted lidars have an important role to play in reducing costs of energy generated by wind turbines. This application is discussed in more detail and in broader scope in other lectures. Acknowledgements The authors are grateful for the support and enthusiasm of their colleagues and collaborators. Without them, wind power lidar technology could not have evolved to its current advanced state. Notation a floating parameter for the fit of the line-of-sight velocity A beam radius at the output lens ADC analogue-to-digital converter b floating parameter for the fit of the line-of-sight velocity B wind bearing c speed of light floating parameter for the fit of the line-of-sight velocity CFD computational fluid dynamics CLR coherent laser radar CNR carrier-to-noise ratio CW continuous wave DFT digital Fourier transform D(ν) power spectral density from dark noise ELO LO field Es stable signal field FFT fast Fourier transform FPGA field-programmable gate array h Planck constant i fluctuating detector power output IR infrared LO local oscillator m slope of the linear regression Ps time-average optical signal power PT transmitted laser power R distance of the beam focus from the lidar output lens R2 coefficient of determination RIN laser relative intensive noise D(ν power spectral density from RIN SNR signal-to-noise ratio t time variable TI turbulence intensity TKE turbulent kinetic energy u wind speed component in the x-direction v wind speed component in the y-direction VAD velocity-azimuth-display VH horizontal wind speed VLOS line-of-sight wind speed w wind speed component in the z-direction 100 <strong>DTU</strong> Wind Energy-E-Report-0029(EN)
References x horizontal position in longitudinal direction y horizontal position in transverse direction z position perpendicular to the horizontal plane β atmospheric backscatter coefficient Γ half-width of the lidar’s peak sensitivity δ lidar’s tilt angle δν Doppler shift in frequency ∆ target distance from the focus position along the beam direction η lidar efficiency θ lidar’s cone half-angle λ laser wavelength ν laser frequency φ lidar’s azimuth angle σ standard deviation ωLO local oscillator frequency ωs stable signal frequency 〈X〉 ensemble average of a variable X Angelou N.,Mikkelsen T.,Hansen K. H.,Sjöholm M.,and HarrisM.(2010) LIDAR WindSpeed Measurements from a Rotating Spinner: SpinnerEx 2009. Project Upwind and Risø report 34: Risø-R-1741(EN) Banakh V. A., Smalikho I. N., Köpp F., and Werner C. (1993) Representativeness of wind measurements with a CW Doppler lidar in the atmospheric boundary layer. Appl. Opt. 34:2055–2067 Banakh V. A., Smalikho I. N., Köpp F., and Werner C. (1999) Measurements of turbulent energy dissipation rate with a CW Doppler lidar in the atmospheric boundary layer. J. Atmos. Oceanic Tech. 16:1044–1061 Barker W. (2009) Analysis of ZephIR data from Mason City, Iowa. Natural Power internal report Barker W., Pitter M., Burin de Roziers E., Harris M., and Scullion R. (2012) Can lidars measure turbulence. EWEA conference PO74 Bingöl F., Mann J., and Foussekis D. (2008) Lidar error estimation with WAsP engineering. IOP Conf. Series: Earth and Environ. Sci. 1:012058 Bingöl F., Mann J., and Foussekis D. (2009) Conically scanning lidar error in complex terrain. Meteorol. Z. 18:189–195 Bingöl F. (2010) Complex terrain and wind lidars. Risø-PhD-52(EN), Roskilde Bleaney B. I. and Bleaney B. (1976) Electricity and magnetism, Oxford University Press, Section 23.4 Cayla M. (2010) Comparison of ZephIR measurements against cup anemometry and power curve measurements. Natural Power internal report (http://www.zephirlidar.com/resources/publications) Chanin M. L., Gariner A., Hauchecorne A., and Portneuve J. (1989) A Doppler lidar for measuring winds in the middle atmosphere. Geophys. Res. Lett. 16:1273–1276 Clifford S. F. and Wandzura S. (1981) Monostatic heterodyne lidar performance: the effect of the turbulent atmosphere. Appl. Opt. 20:514–516 Courtney M. and Gottschall J. (2010) ZephIR 145 validation test. Risø report for Natural Power Hardesty R. M. and Weber B. F. (1987) Lidar measurement of turbulence encountered by horizontal-axis wind turbines. J. Atmos. Oceanic Tech. 67:191–203 Harris M., Pearson G. N., Hill C. A., and Vaughan J. M. (1994) Higher moments of scattered light fields by heterodyne analysis. Appl. Opt. 33:7226–7230 Harris M., Constant G., and C Ward (2001) Continuous-wave bistatic laser Doppler wind sensor. Appl. Opt. 40:1501–1506 Harris M., Pearson G. N., Ridley K. D., Karlsson C. J., Olsson F. A., and Letalick D. (2001) Single-particle laser Doppler anemometry at 1.55 µm. Appl. Opt. 40:969–973 Harris M., Hand M., and Wright A. (2006) Lidar for turbine control. Tech. Report NREL/TP-500-39154 Harris M., Bryce D. J., Coffey A. S., Smith D. A., Birkemeyer J., and Knopf U. (2007) Advance measurements of gusts by laser anemometry. J. Wind Eng. Ind. Aerodyn. 95:1637–1647 <strong>DTU</strong> Wind Energy-E-Report-0029(EN) 101
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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
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
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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
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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
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Page 97 and 98: Figure 56: Normalized power curves
Page 99: the concept. Developments include i
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
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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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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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