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5.2.4 Coherent detection . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 5.2.5 Lidar equation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 5.2.6 Spectral processing MLE . . . . . . . . . . . . . . . . . . . . . . . . 110 5.2.7 Wind vector reconstruction . . . . . . . . . . . . . . . . . . . . . . . 111 5.2.8 Fiber lidars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 5.3 Lidar performances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 5.3.1 Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 5.3.2 Best Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 5.3.3 Distance range and resolution. . . . . . . . . . . . . . . . . . . . . . 114 5.3.4 Velocity range and resolution . . . . . . . . . . . . . . . . . . . . . . 115 5.3.5 Time-bandwidth tradeoffs . . . . . . . . . . . . . . . . . . . . . . . . 116 5.3.6 Existing systems and actual performances . . . . . . . . . . . . . . . 117 5.3.7 Validation of measurements . . . . . . . . . . . . . . . . . . . . . . . 117 5.4 Conclusions and perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6 Remote sensing for the derivation of the mixing-layer height and detection of low-level jets 122 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 6.2 Mixing-layer height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 6.2.1 Acoustic detection methods (Sodar) . . . . . . . . . . . . . . . . . . 123 6.2.2 Optical detection methods . . . . . . . . . . . . . . . . . . . . . . . 127 6.2.3 RASS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 6.2.4 Further techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 6.2.5 Comparison of acoustic and optical MLH detection algorithms . . . . 134 6.3 Boundary-layer height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 6.4 Low-level jets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 6.4.1 Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 6.4.2 Frequency and properties of low-level jets . . . . . . . . . . . . . . . 136 6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 7 What can remote sensing contribute to power curve measurements? 143 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 7.2 Power performance and wind shear . . . . . . . . . . . . . . . . . . . . . . . 143 7.2.1 Shear and aerodynamics . . . . . . . . . . . . . . . . . . . . . . . . 143 7.2.2 Consequences on the power production . . . . . . . . . . . . . . . . 146 7.3 Wind speed profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 7.4 Equivalent wind speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 7.4.1 Standard power curve for the two groups of wind profiles . . . . . . . 150 7.4.2 A better approximation of the kinetic energy flux . . . . . . . . . . . 151 7.4.3 Equivalent wind speed definition . . . . . . . . . . . . . . . . . . . . 152 7.4.4 Choice of instrument . . . . . . . . . . . . . . . . . . . . . . . . . . 152 7.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 6 DTU Wind Energy-E-Report-0029(EN)
8 Nacelle-based lidar systems 157 8.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 8.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 8.3 The units of the lidar scanner . . . . . . . . . . . . . . . . . . . . . . . . . . 157 8.3.1 Windcube . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 8.3.2 Scanner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 8.4 Scan pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 8.5 CNR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 8.6 Wind field reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 8.7 “Visited” test sites of the SWE Scanner . . . . . . . . . . . . . . . . . . . . 163 8.7.1 Onshore test site Bremerhaven, Germany . . . . . . . . . . . . . . . 163 8.7.2 Onshore test site Risø Campus - DTU Wind Energy, Denmark . . . . 164 8.7.3 Onshore test site National Wind Technology Center (NWTC) - NREL, USA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 8.7.4 Offshore test site “alpha ventus” . . . . . . . . . . . . . . . . . . . . 165 8.8 Measurement campaigns and some results . . . . . . . . . . . . . . . . . . . 166 8.8.1 Equivalent wind speed . . . . . . . . . . . . . . . . . . . . . . . . . . 166 8.8.2 Rotor effective wind speed . . . . . . . . . . . . . . . . . . . . . . . 167 8.9 Outlook & Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 9 Lidars and wind turbine control – Part 1 171 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 9.2 Model Based Wind Field Reconstruction . . . . . . . . . . . . . . . . . . . . 171 9.2.1 Ambiguity in Wind Field Reconstruction . . . . . . . . . . . . . . . . 172 9.2.2 Lidar Model for Reconstruction . . . . . . . . . . . . . . . . . . . . . 172 9.2.3 Wind Model for Collective Pitch Control . . . . . . . . . . . . . . . . 173 9.2.4 Wind Model for Cyclic Pitch Control . . . . . . . . . . . . . . . . . . 173 9.2.5 Wind Model for Yaw Control . . . . . . . . . . . . . . . . . . . . . . 173 9.2.6 Wind Model for Complex Terrain . . . . . . . . . . . . . . . . . . . . 173 9.3 Modeling of the Wind Turbine . . . . . . . . . . . . . . . . . . . . . . . . . 174 9.3.1 Reduced Nonlinear Model . . . . . . . . . . . . . . . . . . . . . . . . 174 9.3.2 Estimation of the Rotor Effective Wind Speed from Turbine Data . . 175 9.3.3 Linear Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 9.4 Correlation of a Lidar System and a Wind Turbine . . . . . . . . . . . . . . . 176 9.4.1 Simulated Lidar Measurements . . . . . . . . . . . . . . . . . . . . . 176 9.4.2 Reconstruction of Rotor Effective Wind Speed . . . . . . . . . . . . . 176 9.4.3 Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 9.5 Lidar Assisted Collective Pitch Control . . . . . . . . . . . . . . . . . . . . . 178 9.5.1 Controller and Adaptive Filter Design . . . . . . . . . . . . . . . . . 178 9.5.2 Field Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 9.6 Lidar Assisted Speed Control . . . . . . . . . . . . . . . . . . . . . . . . . . 180 9.6.1 Controller Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 9.6.2 Simulation Using Real Data . . . . . . . . . . . . . . . . . . . . . . . 181 9.6.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 9.7 Nonlinear Model Predictive Control . . . . . . . . . . . . . . . . . . . . . . . 182 9.7.1 Controller Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 9.7.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 9.8 Lidar Assisted Cyclic Pitch Control . . . . . . . . . . . . . . . . . . . . . . . 184 9.9 Lidar Assisted Yaw Control . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 9.9.1 Simulation Using Generic Wind . . . . . . . . . . . . . . . . . . . . . 186 9.9.2 Simulation Using Real Data . . . . . . . . . . . . . . . . . . . . . . . 187 DTU Wind Energy-E-Report-0029(EN) 7
Page 1 and 2: Remote Sensing for Wind Energy DTU
Page 3 and 4: Author: Alfredo Peña, Charlotte B.
Page 5: 4 Introduction to continuous-wave D
Page 9 and 10: 12 Complex terrain and lidars 231 1
Page 11 and 12: 1 Remote sensing of wind Torben Mik
Page 13 and 14: Figure 2: Calibration, laboratory w
Page 15 and 16: Figure 3: Example of scatter plots
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
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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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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
Page 273 and 274:
Figure 185: Temperature profiles in
Page 275 and 276:
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
Page 293 and 294:
Badger M., Hasager C. B., Thompson
Page 295 and 296:
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
Page 307 and 308:
References Bourassa M.A., Legler D.
Page 309:
DTU Wind Energy Technical Universit
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