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Simley, E., Pao, L. Y., Kelley, N., Jonkman, B., and Frehlich, R. (2012) LIDAR wind speed measurements of evolving wind fields. Proc. AIAA Aerospace Sciences Meeting, Nashville, TN Simley, E. and Pao, L. Y. (2013a) Correlation between rotating LIDAR measurements and blade effective wind speed. Proc. AIAA Aerospace Sciences Meeting, Grapevine, TX Simley, E. and Pao, L. Y. (2013b) Reducing lidar wind speed measurement error with optimal filtering. Proc. American Control Conference, Washington, D.C. Simley, E., Pao, L. Y., Frehlich, R., Jonkman, B., and Kelley, N. (2013c) Analysis of light detection and ranging wind speed measurements for wind turbine control. Wind Energy Slinger, C. and Harris, M. (2012) Introduction to continuous-wave Doppler lidar. Proc. Summer School in Remote Sensing for Wind Energy, Boulder, CO Wang, N., Johnson, K., and Wright, A. (2012a) Combined feedforward and feedback controllers for turbine power capture enhancement and fatigue loads mitigation with pulsed lidar. Proc. AIAA Aerospace Sciences Meeting, Nashville, TN Wang, N., Johnson, K., and Wright, A. (2012b) FX-RLS-based feedforward control for lidar-enabled wind turbine load mitigation. IEEE Transactions on Control Systems Technology 20:1212–1222 WT Perf (2012). NWTC Design Codes (WT Perf by Andrew Platt). http://wind.nrel.gov/designcodes/simulators/wtperf/. Last modified 26-November-2012 220 DTU Wind Energy-E-Report-0029(EN)
11 Lidars and wind profiles Alfredo Peña DTU Wind Energy, Risø Campus, Roskilde, Denmark 11.1 Introduction Windlidarshavebeenabletoobservewindprofilessincethe70’sandwerefirstcommercialized in 2005. The ZephIR continuous wave (cw) lidar, nowadays manufactured by Natural Power, entered the wind energy community to compete against the traditional instrumentation, such as cup anemometers and wind vanes, offering in advantage the measurement of wind speed and direction profiles up to 200 m above ground level (AGL), avoiding the flow distortion effects that the traditional instruments suffer when they are mounted on structures. The performance of the ZephIR, when compared with cup anemometers at several heights up to about 100 m, showed high agreement from first studies over land (Smith et al., 2006) and over the sea (Kindler et al., 2007). Later, observations from cup anemometers were combined with ZephIR measurements at the Nysted (Antoniou et al., 2006) and at the Horns Rev (Peña et al., 2009) offshore wind farms to reproduce wind profiles up to about 160 m above mean sea level (AMSL). Although the results from the campaign at Horns Rev showed good agreement with the wind profile theory, limitations on the measurement range were found due to the contamination of the lidar’s Doppler spectra by clouds, which gave the opportunity to Natural Power to improve the cloud correction algorithms of the ZephIR. Since we are interested in wind profile retrieval within 30–200 m where large wind turbines operate, cloud contamination is a serious concern. In fact, when this issue was first addressed, the role of the aerosol profile on the lidar’s probe volume (for any kind of lidar) became more important, specially since the expertise on this subject is rather limited. Mist and fog have also been realized as serious hazards for cws lidars (Peña et al., 2012), which for wind profile analysis results in high wind shears close to the ground, i.e. that–for example, wind profiles observed under stable atmospheric conditions might be interpreted as stable profiles. Nowadays, many more wind lidars such as the Windcube and Galion systems, from the companies LeoSphere and Sgurr Energy, respectively, are also in the market. Both lidars offer instantaneous wind profile observation up to about 10000 m (there is a broad range of types with different ranges), but the instruments’ range actually depends on the atmospheric conditions, namely on the amount of aerosols in the atmosphere, which is proportional to the signal-to-noise ratio (SNR). This is important as the SNR from wind lidars has been used for boundary-layer height (BLH) detection (Peña et al., 2013). As with the ZephIR, a number of campaigns combining observations from cup and sonic anemometers at highmeteorologicalmasts and from pulsed lidars have been carried out. Peña et al. (2010b) described the neutral wind profile and Peña et al. (2010a) the diabatic wind profile, both for homogenous and flat terrain up to 300 m AGL, both using the Windcube to extend wind speed observations from traditional meteorological instrumentation at the National Test Station for Large Wind Turbines (NTWT) at Høvsøre, Denmark. Other meteorological campaigns have looked even further; Floors et al. (2013) illustrated an intercomparison between wind speed observations from a long-range wind lidar and a numerical model up to 800 m and Peña et al. (2013) showed wind speed and direction profiles of the same instrument up to 1000 m. Such studies do not only help for increasing By wind profile, it is meant the vertical wind speed profile High agreement here refers to 1 : 1 comparisons of wind velocity observations with linear correlation coefficients close to 1 By wind shear, it is meant the vertical wind shear DTU Wind Energy-E-Report-0029(EN) 221
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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
Page 169 and 170: Notation C number of sent photons C
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Page 173 and 174: for three unknowns, it is impossibl
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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
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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
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Page 215 and 216: Magnitude (abs) blade pitch gen spe
Page 217 and 218: • Measurement coherence, which ca
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Page 223 and 224: z [m] 160 100 80 60 40 20 10 15 20
Page 225 and 226: z [−] zo 1 κ ln 40 38 36 34 32 3
Page 227 and 228: z [m] z [m] 1000 900 800 700 600 50
Page 229 and 230: the growth of the length scale, agr
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Page 233 and 234: Figure 158: The ZephIR models which
Page 235 and 236: Uconst wΑx l h Φ h.tanΦ Figure 1
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Page 239 and 240: Figure 163: Panahaiko: The scatter
Page 241 and 242: References Albers A. and Janssen A.
Page 243 and 244: Wexler (1968), where the limitation
Page 245 and 246: 13.2.1 Systematic turbulence errors
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Page 249 and 250: The theoretical systematic errors a
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Page 253 and 254: Height (m) Height (m) 160 140 120 1
Page 255 and 256: RMSPE (%) 70 60 50 40 30 20 10 0 vu
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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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