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10 Lidars and wind turbine control – Part 2 Eric Simley 1 , Fiona Dunne 1 , Jason Laks 2 , and Lucy Y. Pao 1 1 Department of Electrical, Computer, and Energy Engineering, University of Colorado, Boulder, CO, USA 2 National Renewable Energy Laboratory, Golden, CO, USA 10.1 Introduction Modern utility-scale wind turbines are typically controlled using yaw, generator torque, and blade pitch actuation (Pao and Johnson, 2011). The yaw motor is used to align the rotor with thewinddirectionforthepurposeofmaximizingpowercapture.Generatortorqueiscontrolled duringbelow-rated(region2)operationtomaximizepowercapturebymaintainingtheoptimal tip speed ratio. In above-rated conditions (region 3), the blades are pitched to regulate rotor speed and power capture by limiting the amount of aerodynamic torque produced by the rotor. In addition to regulating rotor speed, region 3 control systems are often designed to reduce structural loads on the turbine caused by turbulence. A block diagram of a wind turbine’s feedback control system is shown in Fig. 131. In region 2, feedback measurements of generatorspeed are used to maintain the optimal tip speed ratio by adjusting generator torque. The generator torque command is traditionally set equal to the square of the generator speed multiplied by a constant whose value is chosen to maintain the optimal tip speed ratio (Pao and Johnson, 2011). If the wind speed is above the turbine’s rated wind speed, the region 3 controller employs blade pitch control to decrease the fraction oftheavailablepowerinthewindextracted bytherotor.Feedbackmeasurementsofgenerator speed are input to a feedback blade pitch controller designed to both regulate rotor speed to the rated value and reduce structural loads caused by fluctuations in wind speed, which act as a disturbance to the wind turbine. With a single feedback measurement of generator speed, all of the blades are usually pitched together using a strategy called collective pitch (CP) control. When additional feedback measurements are available, such as the blade root bending moment at each blade, the blades can be pitched separately using individual pitch (IP) control (Bossanyi, 2005). Utilizing separate pitch commands for each blade allows for the reduction of loads due to variations in wind speed across the rotor disk, especially blade loads at the once-per-revolution (1P) frequency. A drawback to feedback control is that the wind disturbance must first act on the turbine before a corrective control action can be made based on a feedback measurement. To address this delay, feedforward control has been proposed, whereby a preview measurement of the Portions of this chapter originally appeared in Dunne et al. (2012), Simley and Pao (2013a), Simley and Pao (2013b), and Laks et al. (2013). The American Institute of Aeronautics and Astronautics holds the copyright for Dunne et al. (2012) and Simley and Pao (2013a) and the American Automatic Control Council holds the copyright for Simley and Pao (2013b) and Laks et al. (2013). ¡¡¢¨¤¢¡¡¥¨© ¡¡¢ ¡©¡¤¨¨¡ ¡¡¢£¤¥¦ ©¢ Figure 131: Feedback control scenario. 192 <strong>DTU</strong> Wind Energy-E-Report-0029(EN) £©¡ ¡¤¡¢ ¨¨¡¡¢¤©¢¤¢¡¨¨¡©¢©¨¡©¡¥ ¡¨© ¤¢¡¥ §¨©¨¡
¨¦¦§©¡§ ¥§¨¦¦§£ ¡¥¡¦ £¤¥¦ ¦¦§ ¦¦§¡§¦©¦¤¡¥ ¦¤¦§ ¦¡¤¥ §¦¤ ¦¥¦¡¡¦ ¡¢£¤¥¦§ ¤¥§ ¦¦¢¦¥ ¤¥§ ¡¤¡¥ ¡¡¦¦§¥§§¦¡¡¦¥§¤¥¡¢¦¥¦ Figure 132: Combined feedback and feedforward control scenarios with (a) perfect preview measurements and (b) lidar measurements and the effects of wind evolution. wind disturbance is used directly as an input to the controller (see Fig. 132a). Original work in feedforward control focused on regulating power capture in fluctuating wind conditions using preview measurements provided by an upstream meteorological tower (Kodama et al., 1999). Later, research concentrated on using lidar to remotely sense the wind speeds from the turbine nacelle (Harris et al., 2005). Lidar-based control has been investigated for use in both below-rated and above-rated conditions. In below-rated region 2 control, turbine-mounted lidar measurements have been proposed for correcting yaw error (Schlipf et al. 2011; Kragh et al. 2013) and increasing power capture (Schlipf et al. 2011; Wang et al. 2012a; Bossanyi et al. 2012b). Through simulation, it was found that a scanning lidar system can effectively estimate yaw error and fine-tune the yaw alignment of a turbine as long as the inflow is not too complex (Kragh et al., 2013). Lidar-assisted feedforward control in below-rated conditions has been shown to provide a small increase in power capture (0.1−2%). However, the additional structural loads caused by aggressively tracking the optimal tip speed ratio usually outweigh the power capture improvement (Schlipf et al. 2011; Bossanyi et al. 2012b). Recently, much of the lidar-based feedforward control research has focused on regulating rotor speed and mitigating structural loads in above-rated conditions, where potential for significant load reduction has been shown. As with feedback control, lidar-assisted control in region 3 can be classified as either collective pitch or individual pitch control. Collective pitch feedforward control strategies use the lidar measurements to estimate the effective wind speed that the entire rotor will experience to regulate rotor speed and reduce tower and collective blade loads (Wang et al. 2012b; Schlipf et al. 2012b; Bossanyi et al. 2012b). Individual blade pitch control, however, allows for reduction of the loads experienced by each separate blade, which could also be transferred to non-rotating components on the <strong>DTU</strong> Wind Energy-E-Report-0029(EN) 193
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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
Page 141 and 142: 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: 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
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
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.
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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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