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turbine (Bir, 2008). Information from lidar measurements must be used to estimate the wind speeds encountered by each individual blade. One class of controller designs uses information from lidar measurements to estimate the hub-height, horizontal shear, and vertical shear components of the wind, which are in turn used as inputs to the controller (Schlipf et al. 2010; Laks et al. 2011b; Bossanyi et al. 2012b). The other class of controllers uses a separate measurement of the expected wind speed for each individual blade (Dunne et al. 2011b; Laks et al. 2011b; Dunne et al. 2012). During the summer of 2012, a simple lidar-based collective pitch feedforward controller designed to regulate rotor speed was tested at the US National Renewable Energy Laboratory (NREL) with promising results (Scholbrock et al., 2013). The simulation of lidar-based controllers is progressing toward more realistic preview measurement modeling. Preliminary research included the assumption that upstream wind speeds could be measured perfectly (Schlipf and Kühn 2008; Dunne et al. 2011b; Laks et al. 2011b). An additional assumption was that the measured wind speeds would reach the turbine unchanged after a delay time of d/U, where d is the preview distance and U is the mean wind speed(seeFig.132a).Thenextgenerationoffeedforwardcontrolsimulationscontainedrealistic lidar models including spatial averaging of the wind speeds along the lidar beam (discussed in Section 10.5.1) (Schlipf et al. 2009; Dunne et al. 2011a; Laks et al. 2011a; Simley et al. 2013c; Kragh et al. 2013). Recently, control simulations have included models of the important source of error commonly referred to as “wind evolution” (discussed in Section 10.5.3) (Bossanyi 2012; Laks et al. 2013). Wind evolution describes how the wind speeds change, or evolve, as they travel downstream toward the turbine. The more realistic lidar measurement scenario including wind evolution is shown in Fig. 132b. An additional source of measurement error that has recently been included in simulation is uncertainty in the time it takes for the measured wind to reach the rotor (Mirzaei et al., 2013). 10.1.1 Lidar Measurement Strategies Several lidar technologies suitable for wind turbine control have been field tested in the past decade. Harris et al. (2007) performed the first turbine-mountedlidar test using a continuouswave (CW) lidar (see Section 10.5.1) mounted on top of the nacelle. This lidar was simply configuredtostarestraightaheadintothewindandfocusataparticulardistance.Rettenmeier etal.(2010)employacustomizedpulsedlidarsystem(seeSection10.5.1)thatisalsomounted on top of the nacelle. The pulsed lidar provides measurements at a number of points along the beam and can be aimed in any direction in front of the rotor using a mirror with two axes of motion. Mikkelsen et al. (2013) have developed a circularly scanning CW lidar that can be mounted in the hub, or spinner, of a turbine (see Fig. 133). This configuration allows for continuous measurements without periodic blockage from passing blades. Recently, Pedersen et al. (2013) have tested a CW lidar mounted directly on a turbine blade, allowing for direct measurement of the wind speed and angle-of-attack that a rotating blade will experience. The lidar measurement analyses in the rest of this chapter assume a circularly scanning spinner-mounted lidar similar to the system developed by Mikkelsen et al. (2013). Figure 133 containsanillustrationofthe measurementscenario.Thehub-mountedlidarmeasuresacircle ofwindwithscanradiusr atapreviewdistancedupstreamoftherotor.Althoughitispossible to scan faster than the rotational speed of the rotor (Mikkelsen et al., 2013), the analyses in this chapter assume that the lidar spins with the rotor, allowing the lidar to measure the wind that a single blade will experience. The relative simplicity of this measurement scenario allows for careful analysis of measurement performance. Unless otherwise specified, the lidar investigations in this chapter are performed using the NREL 5-MW reference turbine model (Jonkman et al., 2009) which has a hub height of 90 m and a rotor radius of 63 m. The remainder of this chapter is organized as follows. Section 10.2 presents the region 3 feedforward control scenario using a linear wind turbine model. The feedforward control strategy that minimizes the variance of an output error variable such as generator speed or a structural load is derived for the case of imperfect lidar measurements. An explanation of why measurement preview is required in feedforward wind turbine control is presented in 194 <strong>DTU</strong> Wind Energy-E-Report-0029(EN)
vertical (m) 150 100 50 R d 0 0 -50 -100 along wind (m) ¡©£§¥§ r 50 -50 0 horizontal (m) ¡¢£¤¥¦£§¨© Figure 133: Measurement scenario for the 5-MW reference turbine. The variable d represents the preview distance upwind of the turbine where measurements are taken. r represents the radius of the scan circle and R indicates the 63 m rotor radius. The dots represent where measurements are taken for the circularly scanning lidar and the magenta curve illustrates the range weighting function along the lidar beam (see Section 10.5.1). Section10.3.Section10.4describesthewindspeedquantityofinterestforbladepitchcontrol, referred to as “blade effective wind speed.” Section 10.5 describes how measurements from a hub-mounted lidar can be used to estimate blade effective wind speed. The main sources of lidar measurement error are discussed here as well. The topics in Sections 10.2, 10.4, and 10.5 are combined to illustrate a lidar measurement design scenario in Section 10.6, with the objective of minimizing the mean square value of blade root bending moment using feedforward control. Examples of lidar-based feedforward controller designs using simulation areincludedinSections10.7and10.8.Finally,Section10.9summarizesthematerialpresented in the chapter. 10.2 Wind Turbine Feedforward Control The combined feedback/feedforward control scenario for above-rated conditions used in this chapter is described in Fig. 134. A blade pitch control loop using feedback from rotor speed regulates an output variable y (such as generator speed error or a structural load), which represents the deviation from a set point. Generator torque is controlled to be inversely proportionalto generatorspeed to maintain the rated 5 MW of power(Jonkman et al., 2009). The feedback control loop is designed for the mean wind speed U such that any wind speed deviationactsasadisturbancewt ontheturbine.Thefeedbackcontrolloopisaugmentedwith a blade pitch feedforward controller F which uses the preview wind disturbance measurement wm, provided by a lidar. Additionally, a prefilter Hpre can be introduced between the lidar measurementandthefeedforwardcontrollertoprovideanestimate ˆwt ofthewinddisturbance based on wm. The originalupstream wind worig is measured by the lidaryielding the distorted measurement wm. As worig travels downstream toward the turbine, it will undergo distortion due to wind evolution until it interacts with the turbine as wt after a delay of d/U. 10.2.1 Feedforward Control Assuming Perfect Measurements When feedforward is not used (F = 0), the output variable y is given by y = Tywtwt u v w (214) <strong>DTU</strong> Wind Energy-E-Report-0029(EN) 195
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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)
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 and 168: Figure 112: Power curve met mast an
Page 169 and 170: Notation C number of sent photons C
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 197 and 198: With feedback only, on the other ha
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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
Page 219 and 220: Jonkman, B. (2009) TurbSim user’s
Page 221 and 222: 11 Lidars and wind profiles Alfredo
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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
Page 231 and 232: 12 Complex terrain and lidars Ferha
Page 233 and 234: Figure 158: The ZephIR models which
Page 235 and 236: Uconst wΑx l h Φ h.tanΦ Figure 1
Page 237 and 238: Figure 162: Lavrio: The scatter plo
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
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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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