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of the rotor effective wind speed v0(t) is a weighted average over all available vi available during time t (Figure 114(right)). That means that the five focus distances are shifted to one single distance. This shift can be done by the use of Taylor’s frozen turbulence hypothesis which is valid for horizontally Lidar measurements (Schlipf et al., 2010). Figure 114: Circle trajectory used for the CART-2 campaign (left) and schematic drawing of the incoming wind field measured by a lidar at different focus distances (right). In (Figure 115(left)) three different wind speeds are shown in a 10-min time series: one is measured by the anemometer on the met mast vM, the other one shows the rotor effective wind speed v0L measured with the Lidar. The third wind speed is recalculated from the turbine data as e.g. the pitch angle, rotor speed and power output data. Here the turbine represents a big horizontal axes anemometer with a rotor effective wind speed v0. It is visible that the turbine’s wind speed corresponds much better to the rotor effective wind speed of the Lidar than to the anemometer on the met mast.In a further step, the power production of the CART-2 turbine was simulated with FAST. As input for the wind two different data sets were used: the real met mast data and the rotor effective wind speed measured with the Lidar. In (Figure 115(right)) the result of the simulated power output is shown together with the real electrical power from the turbine. As one can see, the simulation with the rotor effective wind speed corresponds very well with the real data (Rettenmeier et al., 2012b). Figure 115: Time series of the rotor effective wind speed from Lidar and turbine and of the anemometer mounted on a mast (left) and simulation of the power production with met mast and Lidar data in comparison with the real production data. (right). 8.9 Outlook & Conclusions The use of a lidar system from the nacelle offers various applications in wake wind field analysis, wind turbine control, power curve determination and load estimation. The whole swept rotor area can be taken into account, but assumptions have to be made. Nacelle-based measurement methods show a great potential on- and offshore and even in complex terrain. But there are still some investigations that have to be done concerning turbulence, vertical and horizontal shear, comparison criteria and equivalent wind speed. 168 <strong>DTU</strong> Wind Energy-E-Report-0029(EN)
Notation C number of sent photons CNR carrier to noise ratio DEWI German wind energy institute i index of layers of points m beam focus plane nlos line of sight normal vector nwindir normal wind direction vector N number of reflected photons and number of layers Pi beam focus area points SNR signal to noise ration SWE Stuttgart Chair of Wind Energy ubigcross 5-point average wind speed ulos wind speed component in x-direction umean 10-min mean wind speed up longitudinal wind speed at point P vi average wind speed of trajectory layer i vlos line-of-sight velocity vm wind speed measured on met mast v0 rotor effective wind speed, recalculated from turbine data v0L rotor equivalent wind speed, calculated by lidar data vp transversal wind speed at point P WiTLiS wind turbine simulator wp vertical wind speed at point P xn longitudinal coordinate of normal vector nlos xp longitudinal coordinate of point P yn transversal coordinate of normal vector nlos yp transversal coordinate of point P zn vertical coordinate of normal vector nlos zp vertical coordinate of point P ϕ angle between nlos and nwinddir References Bischoff O. Hofsäss M, Rettenmeier A., Schlipf D. and Siegmeier B, (2010) Statistical load estimation using a nacelle-based lidar system. DEWEK, Bremen Canadillas B., Schlipf D., Neumann T. and Kuehnel D. (2011) Validation of Taylor’s hypothesis under offshore conditions. EGU, Vienna Cariou J.-P. and Boquet M. (2010) Leosphere pulsed lidar principles. Contribution to EU-project UpWind WP6 Harris M., Hand M. and Wright A. (2006) lidar for turbine control. Tech Report NREL/TP-500-39154 Klausmann P. (2010) Calculation of power curves based on interpolated line-of-sight velocities of nacelle-based lidar-measurements. Study thesis, SWE Mikkelsen T., Hansen K., Angelou N., Sjöholm M., Harris M., Hadley P., Scullion R., Ellis G. and Vives G. (2010) lidar wind speed measurements from a rotating spinner. EWEC, Warsaw Rettenmeier A., Bischoff O. Hofsäss M, Schlipf D. and Trujillo J. J. (2010) Wind field analysis using a nacelle-based lidar system. EWEC, Warsaw Rettenmeier A., Klausmann P., Bischoff O., Hofsäss M, Schlipf D., Siegmeier B. and Kühn M. (2011) Determination of Power Curves Based on Wind Field Measurements Using a Nacelle-based Lidar Scanner. EWEA, Brussels Rettenmeier A., Wagner R., Courtney M., Mann J., Bischoff O., Schlipf D., Anger J., Hofsäss M. and Cheng P. W. (2012) Turbulence and wind speed investigations using a nacelle-based Lidar scanner and a met mast. EWEA, Copenhagen Rettenmeier A., Schlipf D., Würth I., Cheng P. W., Wright A., Fleming P., Scholbrock A., Veers P. (2012) Power Performance Measurements of the NREL CART-2-WindTurbine Using a nacelle-based Lidar scanner. ISARS, Boulder Schlipf D., Trujillo J. J., Basterra V. and Kühn M. (2009) Development of a wind turbine lidar simulator. EWEC, Marseille <strong>DTU</strong> Wind Energy-E-Report-0029(EN) 169
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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
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
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: Figure 112: Power curve met mast an
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
Page 191 and 192: E. Hau, Windkraftanlagen, 4th ed. S
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Page 195 and 196: vertical (m) 150 100 50 R d 0
Page 197 and 198: With feedback only, on the other ha
Page 199 and 200: Figure 136: Estimated preview requi
Page 201 and 202: Normalized C r = C r P Q 2 W (r) b
Page 203 and 204: Normalized C r = C r P Q 2 W (r) b
Page 205 and 206: Coherence 1 0.8 0.6 0.4 0.2 0 10
Page 207 and 208: Coherence 1 0.8 0.6 0.4 0.2 0 10
Page 209 and 210: Magnitude Squared 10 8 10 7 10 6 10
Page 211 and 212: Figure 148: During simulation, FAST
Page 213 and 214: Figure 149: Collective flap respons
Page 215 and 216: Magnitude (abs) blade pitch gen spe
Page 217 and 218: • 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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