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Figure 191: Envisat ASAR wind field observed on 20 February 2009 at 22:22 UTC. The wind map shows acceleration flow (gap wind) through and behind the Gibraltar Strait. The wind is from the east. Figure 192: Wind farm wake at Horns Rev 1-/2 offshore wind farms in the North Sea observed from RADARSAT-1 from MacDonald, Dettwiler and Associates Ltd., 2012. From EERA DTOC project. Courtesy: Alexis Mouche, CLS. are complicated and ocean wind observations are sparse. Time-series data from meteorologicalmasts typically include observations for every 10 minutes or each hour for a least one year. In this wayone is certain to capture the wind conditions in all seasonsboth dayand night. It is necessaryto evaluate to what degree the one-yeartimeseries is representative for the longer term (future 30 years). Long-term correlation/correction is not trivial; it adds some uncertainty to the wind resource (Hasager et al., 2008). 286 DTU Wind Energy-E-Report-0029(EN)
Satellites in sun-synchronous polar orbit observe a given local area at a certain local time in ascending (northbound) track and 12-hour shifted in descending (southbound) track. There are differences in the local acquisition time of the various satellites carrying radar instruments. If a local site has pronounced daily wind speed variations, the two fixed observational times may not be fully representative of the diurnal cycle. Far offshore diurnal variation is typically less pronounced than in some coastal areas, e.g. sites with pronounced land-sea breezes. Using observations from several satellites with different fixed observational times would be a possibility, but this has not yet been achieved. Another challenge of using SAR for wind resource assessment is the relatively few observations available. For some sites less than 200 scenes may be available. In European coastal waters Envisat provides a somewhat higher number of observation. In the later years of operation many wide swath mode scenes were recorded. This makes it a unique archive for wind resource assessment. The available scenes can be seen at http://earth.esa.int/EOLi. Barthelmie & Pryor (2003) and Pryor et al. (2004) investigated based on statistical analysis and using offshore meteorological observations from the Baltic Sea and North Sea, the number of randomly sampled wind observations necessary to carry out wind resource analysis within certain statistical bounds. The conclusion was that around 70 samples are sufficient to estimate mean wind speed and the Weibull scale parameter but around 2,000 samples are needed to estimate energy density and the Weibull shape parameter at the 10% confidence level (significance 95%). The result makes it clear that the accuracy of SAR-based wind resource statistics is adequate for pre-feasibility studies. In other words, the SAR-based wind resource map may be used as guide to site an offshore meteorological mast in a wind farm project. Alternatively, if high-quality offshore observations exist, the local wind gradients at 10 m above sea level may be evaluated from SAR wind resource maps. Also, combining or blending SAR wind resource statistics and mesoscale wind resource modelling is an option (Badger et al., 2010b). When planning clusters of wind farms that cover 100 km 2 or more, it is advisable to have two meteorological masts if the wind resource should be assessed as accurately as possible. It is most likely that significant wind gradients exist within such areas, in particular in coastal zones (Barthelmie et al., 2007). At Horns Rev the first wind farm is located 14 to 21 km offshore from the closest coastal point and the second wind farm is located 26 to 31 km offshore. Using SAR, winds at 1 − 2 km grid scale allow quantification of the local wind gradients. A third wind farm at Horns Rev in the North Sea is in planning. 15.10 S-WAsP In-house software was developed for SAR wind resource wind mapping at DTU Wind Energy. The Satellite-WAsP (S-WAsP) software reads SAR wind fields from ANSWRS and SARTool (SOPRANO). In earlier versions of S-WAsP other formats were used as input (Nielsen et al., 2004; Hasager et al., 2008). The first step is to ensure geographical collocation of the series of wind fields (through a database) and select the desired data. The second step is to run the wind resource application routines to calculate the wind resource statistics: mean wind speed, Weibull scale, Weibull shape, and energy density and the statistical uncertainty for each parameter. The final results are output maps of the parameters (Hasager et al., 2012). The number of satellite observations in a single directional bin may be small. Therefore to make it possible to fit a distribution, all data is used to derive the shape parameter. The shape parameter is assumed valid for every bin. The Weibull scale parameter is then estimated by the average wind speed in each sector. The frequency ofoccurrence in each sectoris uncertain when observations are sparse and there is a risk of observing sectors without any observation and no estimate of the mean wind. An alternative to simple bin counting is to sort all observations after directions, estimate the probability density between the observations by the angle separating between them, and finally resample the densities in the standard sectors (Nielsen et al., 2004). The available wind powerdensity, E W/m 2 that is proportional to the wind speed cubed, DTU Wind Energy-E-Report-0029(EN) 287
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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
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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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Page 241 and 242: References Albers A. and Janssen A.
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Page 255 and 256: RMSPE (%) 70 60 50 40 30 20 10 0 vu
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Page 275 and 276: References s point in space Sν(s)
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Page 283 and 284: Figure 188: Envisat ASAR wind field
Page 285: Figure 190: Envisat ASAR wind field
Page 289 and 290: 500 overlapping scenes for wind res
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Page 295 and 296: Ren Y. Z., Lehner S., Brusch S., Li
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Page 299 and 300: The modified logarithmic wind profi
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Page 303 and 304: QuikSCAT 25 20 15 10 5 Horns Rev Fi
Page 305 and 306: Figure 203:Example of an ASCAT coas
Page 307 and 308: References Bourassa M.A., Legler D.
Page 309: DTU Wind Energy Technical Universit
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