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calibrated cup anemometer. A significant source for uncertainty with RS instrumentation relative to a cup anemometer, and for sodars in particular, is the remote instrument’s relative big measurement volumes. A sodar measuring the wind speed from say 100 m height probes a total samplingvolumeof more than 1000m 3 , whereas a cup anemometeris essentiallya point measurementdeviceinthisconnection.In additionthesodar’smeasuredwindcomponentsare displacedinspaceandtime,whichmakesthe interpretationofmeasuredturbulencebyasodar impaired. In addition the huge sampling volumes will be putting restrictions on measurements in non-uniform flow regimes such as found near forest edges, on offshore platforms, and over hilly or complex terrain. Sodar’s RS is alsoin demand fordirect turbine controlintegration,windpoweroptimization and turbine mounted gust warning systems, but here the demand on accuracy and reliability is correspondingly high. Today, sodars are typically used to measure 10-min averaged vertical profiles in the height interval between, say 20 and 200 m above the ground, of the following quantities: • Mean wind speed • Mean wind directions (including azimuth and tilt) • Turbulence (all three wind components: longitudinal, transverse and vertical) Albeit significant inherent scatter persists in sodar measured mean wind speed and direction data average mean wind speed compare relatively well (in most cases to within ±3%) to that of a corresponding cup anemometer measured wind speed, cf. the slopes of the scatter plots in Figure 3. However,thecorrelationcoefficientsbetweensodarandcupanemometerdatais,depending on measurement height and atmospheric stability, relative poor as compared to a cup-to cup anemometer correlation, where the two cups are separated by ∼ 100 m (typically less than 0.95)and reflects,amongotherissues, thatamono-staticsodarmeasuresthe windspeed over a huge volume whereas the cup anemometer represents a point measurement. In addition, increased scatter will occur as a result of beam-bending due to the relative big wind speed to propagation speed of the sound pulses. Also notable is that sodars are able to make only a single 3D vector speed measurement about once per 6–10 s. A slow sampling rate also makes the mean prediction of a 10-min averaged quantity uncertain, due to limited independent samplingcounts.Inhisnote“Statisticalanalysisofpoorsamplestatistics”,Kristiansen(2010) has shown that “counting” uncertainty in terms of relative “standard deviation of the sample variance” in a small sample can give rise to a ∼ 10% relative uncertainty when averaged quantities are drawn from a set of only 100 independent samples. It is also seen from the sodar vs. cup anemometer data in Figure 3 that difficulties with the C/N ratio can occur when wind speeds exceed approximately 15 m s −1 , which by the way is a nominal wind speed for a wind turbine. This is due to high background noise and the loss of backscatter in neutrally stratified high wind speed regions. Recently relative good agreements over forested areas have nevertheless been seen (< 1% discrepancy) betweensodarandcup anemometermeanturbulenceintensityhas beenreported by Gustafsson (2008). However, turbulence intensity, which is the stream wise turbulence component relative to the mean wind speed, is in a 10-min averaged quantity dominated, particularly in forested areas, by the most energy containing eddies, which in this case will be larger than the sodar’s sampling volumes and therefore be well represented in the statistics. However, the smaller scales including turbulent eddies with wind gusts must be anticipated to be present also on the scales smaller than a mono-static ground based sodar will be able to capture. While sodars appears to be able to measure accurately both the mean winds speed and the turbulence intensities at a turbines hub height it was found more difficult to use a sodar for accurate measurements over the entire rotor plane due to low C/N ratio (Wagner et al., 2008).There are several sodarmanufactures onthe windRS markettodayincludingforexample Remtech, Atmospheric Systems Corporation (formerly Aerovironment), Metek, Scintec, 14 DTU Wind Energy-E-Report-0029(EN)
Figure 3: Example of scatter plots from sodar vs. cup anemometer data. The upper graph presents unscreened sodar wind speed data plotted against corresponding high-quality cup anemometer data measured at the Risø DTU met tower at 125 m. A data availability corresponding to 76% (9549 10-min averaged wind speeds) was obtained during this particular sodar vs cup anemometer inter-comparison test of almost three month duration (12532 10min periods). The middle data graph shows the same data set after screening of the sodar data for high C/N-ratios. The scatter is significantly reduced, but so is also the data availability which with only 4210 data points has been reduced to almost 34%. The bottom panel shows (left) simultaneous measured sodar vs cup scatter plo tat 75 m height (0.989) and (right) lidar vs the same cup for the same data period. The lidar measurements at 80 m are seen to exhibit less scatter and high correlation coefficient (0.996) DTU Wind Energy-E-Report-0029(EN) 15
Page 1 and 2: Remote Sensing for Wind Energy DTU
Page 3 and 4: Author: Alfredo Peña, Charlotte B.
Page 5 and 6: 4 Introduction to continuous-wave D
Page 7 and 8: 8 Nacelle-based lidar systems 157 8
Page 9 and 10: 12 Complex terrain and lidars 231 1
Page 11 and 12: 1 Remote sensing of wind Torben Mik
Page 13: Figure 2: Calibration, laboratory w
Page 17 and 18: 1.2.3 Summary of sodars Most of tod
Page 19 and 20: 1.3.3 Wind lidars Measuring wind wi
Page 21 and 22: Figure 6: CW wind lidars (ZephIRs)
Page 23 and 24: Further developments Furthermore, n
Page 25 and 26: 2 The atmospheric boundary layer S
Page 27 and 28: Figure 9: Large spatial scale varia
Page 29 and 30: Du3 Dt Du1 Dt Du2 Dt The three mome
Page 31 and 32: Figure 13: Consensus relations betw
Page 33 and 34: ψ z L ∼ − 5 L . For unstable c
Page 35 and 36: Figure 15: Behavior of the turbulen
Page 37 and 38: Figure 17: Newly developed models t
Page 39 and 40: The value of q0 at the surface is d
Page 41 and 42: the spray is the source of icing on
Page 43 and 44: u∗2 u∗1 u1(h) = u∗1 k ln h
Page 45 and 46: Figure 26:Land-seabreeze system,whe
Page 47 and 48: 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
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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
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5.3.6 Existing systems and actual p
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(Gottshall et al., 2010; Albers et
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References Albers A., Janssen A. W.
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derived from fluctuations of the wi
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Acoustic received echo (ARE) method
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Figure 71: Sample time-height cross
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A gradient minimum is characterized
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Figure73:Bragg-relatedacoustic(belo
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stability (inversion strength) can
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Figure 76: Combined soundingwith a
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Figure 79: Favorite regions (shaded
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Direct detection of MLH from acoust
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Engelbart D.A.M.and Bange J. (2002)
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7 What can remote sensing contribut
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uyms 9.0 8.5 8.0 7.5 7.0 120 140 16
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Bottom of rotor Φ rotation r w u
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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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