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Senff C., Bösenberg J., Peters G., and Schaberl T. (1996) Remote Sesing of Turbulent Ozone Fluxes and the Ozone Budget in the Convective Boundary Layer with DIAL and Radar-RASS: A Case Study. Contrib. Atmos. Phys. 69: 161–176 Sicard M., Pérez C., Rocadenbosch F., Baldasano J. M., and García-Vizcaino D. (2006) Mixed-Layer Depth Determination in the Barcelona Coastal Area From Regular Lidar Measurements: Methods, Results and Limitations. Bound.-Layer Meteorol. 119: 135–157 Steyn D. G., Baldi M., and Hoff R. M. (1999) The detection of mixed layer depth and entrainment zone thickness from lidar backscatter profiles. J. Atmos. Ocean Technol. 16: 953–959 White A. B., Senff C. J., and Banta R. M. (1999) A Comparison of Mixing Depths Observed by Ground-Based Wind Profilers and an Airborne Lidar. J. Atmos. Oceanic Technol. 16: 584–590 Wulfmeyer V. (1999) Investigation of turbulent processes in the lower troposphere with water-vapor DIAL and Radar-RASS. J. Atmos. Sci. 56: 1055–1076 Wulfmeyer V. and Janjić T. (2005) 24-h observations of the marine boundary layer using ship-borne NOAA High-Resolution Doppler Lidar. J. Appl. Meteorol. 44: 1723–1744 142 DTU Wind Energy-E-Report-0029(EN)
7 What can remote sensing contribute to power curve measurements? Rozenn Wagner DTU Wind Energy, Risø Campus, Roskilde, Denmark 7.1 Introduction Power performance measurement is central to the wind industry since it forms the basis for the power production warranty of the wind turbine. The power curve measurement has to be realised according to the IEC 61400-12-1 standard. The power curve is obtained with 10-min mean power output from the turbine plotted against simultaneous 10-min average wind speeds. The standard requires the wind speed to be measured by a cup anemometer mountedontop ofamast havingthe same heightasthe turbine huband locatedat a distance equivalent to 2.5 rotor diameters from the turbine. Such a plot usuallyshowsasignificantspread ofvalues and not a uniquelydefined function. The origin of the scatter can mainly be grouped into three categories: the wind turbine components characteristics, sensor error and the wind characteristics. Within the last group, the current standard only requires the wind speed at hub height and the air density measurement. However, other wind characteristics can influence the power production like the variation of the wind speed with height (i.e. wind speed shear). The influence of wind speed shear on the power performance was shown in several studies: some based on aerodynamic simulations (Antoniou, 2009; Wagner et al., 2009) others based on measurements (Elliot and Cadogan, 1990; Sumner an Masson, 2006). A major issue is to experimentally evaluate the wind speed shear. The wind speed profile is usually assumed to follow one of the standard models such as the logarithmic or power law profiles. However, these models are valid for some particular meteorological conditions, and therefore, cannot represent all the profiles experienced by a wind turbine. Measurements are then a better option but are also challenging. Indeed characterising the wind speed profile in front of the rotor of a multi-MW wind turbine requires measurements of wind speed at several heights, including some above hub height, i.e. typically above 100 m. Remote sensing instruments such as lidar or sodar then appear as a very attractive solution. This chapter starts with a description of the influence of the wind speed shear on the power performance of a multi-MW turbine. The challenge of describing the wind speed profile is then discussed followedby a description of an experiment using a lidar for its characterisation. This is followed by the introduction of the definition of an equivalent wind speed taking the wind shear into account resulting in an improvement of the power performance measurement. Finally, some recommendations about remote sensing instruments are given to successfully apply this method. 7.2 Power performance and wind shear 7.2.1 Shear and aerodynamics In order to see the effect of the wind speed shear on a wind turbine, aerodynamic simulations were carried out for two inflow cases: 1. constant wind speed profile (same wind speed everywhere) with 8 m s −1 2. power law profile with 8 m s −1 at hub height and a shear exponent of 0.5 DTU Wind Energy-E-Report-0029(EN) 143
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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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Page 93 and 94: Table 7: Combined results from 28 Z
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Page 101 and 102: References x horizontal position in
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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.
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Page 125 and 126: Acoustic received echo (ARE) method
Page 127 and 128: Figure 71: Sample time-height cross
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Page 131 and 132: Figure73:Bragg-relatedacoustic(belo
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Page 137 and 138: Figure 79: Favorite regions (shaded
Page 139 and 140: Direct detection of MLH from acoust
Page 141: Engelbart D.A.M.and Bange J. (2002)
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
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Page 159 and 160: • Flexibletrajectories.Dependingo
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Page 163 and 164: The normal wind direction vector nw
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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
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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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