- Email: [email protected]
Advance measurement of gusts by laser anemometry
ARTICLE IN PRESS
Journal of Wind Engineering and Industrial Aerodynamics 95 (2007) 1637–1647 www.elsevier.com/locate/jweia
Advance measurement of gusts by laser anemometry Michael Harrisa,, David J. Brycea, Adrian S. Coffeya, David A. Smitha, Jochen Birkemeyerb, Ullrich Knopf b a
Sensors and Electronics Division, QinetiQ Malvern, Worcestershire WR14 3PS, UK b Nordex Energy GmbH, Bornbarch 2, 22848 Norderstedt, Germany
Received 22 June 2004; received in revised form 8 December 2006; accepted 23 February 2007 Available online 11 April 2007
Abstract A coherent laser radar (or Doppler lidar) operating at a wavelength of 1.55 mm has been mounted on the nacelle of a 2.3 MW wind turbine in order to measure the wind speed in front of the blades at ranges up to 200 m. The lidar measures the component of wind speed along its beam direction but, since it rotates with the nacelle to point into the wind, this component normally differs negligibly from the actual wind speed. As an example to demonstrate the lidar’s capabilities, we present samples of wind data gathered over an 18 h period in March 2003, illustrating the development of gusts that appears to be associated with the onset of solar ground heating. Wind speeds over the full period lay in the range 4–10 m s1, and the gusts were evident as a near-discontinuity in wind speed typically of order 1–2 m s1, but reaching values larger than 3 m s1. The typical timescale between gusts was of order 1 min. Improvements to this experiment are proposed that will provide more detailed information on spatial and temporal structure of such gusts. r 2007 Elsevier Ltd. All rights reserved. Keywords: Coherent laser radar; Doppler lidar; Laser anemometry; Gusts; Turbulence
1. Introduction Coherent laser radar (CLR) or Doppler lidar is a technique that involves the emission of a coherent light beam, and detection of the weak return reflected or scattered from a distant target. The technique provides a means to measure the line-of-sight component of Corresponding author. Tel.: +44 1684 895731; fax: +44 1684 896270.
E-mail address: [email protected] (M. Harris). 0167-6105/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.jweia.2007.02.029
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wind speed via detection of the Doppler shift for light backscattered from natural aerosols (particles of dust, pollen, droplets, etc.) in the atmosphere (Vaughan et al., 1996). CLR was first developed in the 1970s using CO2 lasers, and since then has been used primarily as a research tool in applications such as the study of aircraft wake vortices (Harris et al., 2002). Recently, CLR systems based on comparatively cheap off-the-shelf telecommunications equipment operating at a wavelength l of 1.55 mm have been built (Karlsson et al., 2000; Harris et al., 2001) with the potential to offer routine autonomous remote wind speed measurement. We describe measurements made with one such system mounted on the nacelle of a Nordex N90 wind turbine at Postlow, 5 km W of Anklam, N.E. Germany. The aims of this paper are to demonstrate the capabilities of lidar by presenting some selected examples of wind speed data showing interesting behaviour over a 1-day period in March 2003, and hence to alert the wind engineering community to the potential of lidar as a reliable and convenient method for remote anemometry. The data show uniform stable airflow during the early part of the night, followed by the development of a pattern of distinctive repeating gusts. Further experiments are suggested that would provide significantly improved information on the nature of the gusts. Finally, a brief comparison is made of the capabilities and limitations of lidar for anemometry compared with that of more widely deployed sodar techniques. 2. Experiment The lidar is a continuous-wave (CW) monostatic design, capable of measuring line-ofsight wind speeds up to 39 m s1. The basic principles of its operation are found in Karlsson et al. (2000), with more details of this particular system given in Smith et al. (2006). The lidar transceiver was mounted on top of the nacelle of a Nordex N90 wind turbine, and was connected by fibre-optic and electrical cable to a control unit (in the base of the tower) containing the laser source, detector and signal-processing computer. The transmitted laser power would normally be 1 W; however, with the long (120 m) connecting fibres used here the output was restricted to 400 mW in order to avoid the non-linear optical mechanism of stimulated Brillouin scattering (Harrison et al., 1990), which would lead to excessive noise and poor lidar performance. The system could be focused at any range R from 10 m out to 200 m, and it measured a value for wind speed approximately 10 times every second. For the data presented in the next section, the lidar focus was fixed at 200 m. At this setting, the lidar probe volume (a pencil-thin region of space, diameter 10 mm at focus) is of considerable extent, with the relative sensitivity S as a function of range z given to a good approximation by SðzÞ /
L , ðz RÞ2 þ L2
(1)
where R is the range to beam focus and L is the characteristic (Rayleigh) length for the beam to double its area on either side of the focal plane lR2 , (2) pA2 where A is the transmitted beam radius (to 1/e2 intensity). As used here, the lidar sensitivity is highest at R ¼ 200 m, and decays away from the focus as a Lorentzian function with a half-width of L40 m. L
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The terrain around the site is very flat for a considerable distance, consisting of farmland with no large buildings and occasional lines of trees. The lidar beam was aligned roughly parallel to the rotor axis, propagating horizontally at a height 90 m above mean ground level. The system remained installed for a three-week period in March 2003; here we report specifically a series of measurements from a limited period over 4–5 March, during which the wind remained roughly East-South-Easterly. The geometry of the site ensures that under such conditions our wind measurements were not influenced by the presence of other wind turbines situated over 250 m away to the N and S. Because the lidar only measures the line-of-sight component of the wind speed, we relied upon rotation of the nacelle to ensure that the lidar beam always pointed close to the upwind direction. The error from imperfect alignment with wind direction is very small, and is easily derived from the cosine of the angular error (giving, for example, an underestimate of wind speed by 1.5% for 101 of misalignment). Because the lidar beam points forwards from the back of the nacelle, it is blocked approximately once per second by the rotating blades of the turbine. As a result, only 60% of measurements are successful with the beam striking a blade in the remaining 40%. Such occurrences give rise to very strong signals at low frequency (blade motion is nearly orthogonal to the lidar beam, so the Doppler shift is small) and are easily identified and rejected. The detector requires no recovery time from the intense blade reflection, so that blocking presents no real problem in practice, with the lidar obtaining five or six independent wind measurements every second. Weather conditions were stable for the period of measurements presented here, with a consistent ESE wind of mean value 5–7 m s1. The temperature remained close to freezing, with lying snow and night frost. There was little cloud cover with hazy sunshine in the daytime, and visibility was estimated to be 5–10 km giving good uniform scattering that resulted in high signal-to-noise ratio (SNR) for the lidar wind measurements. Fig. 1 shows typical wind Doppler spectra, acquired via a 512-point fast Fourier transform (FFT) process. The raw data-sampling rate is 100 M samples s1, and power spectra derived from 256 individual FFTs are averaged to produce each spectrum, which represents a measurement time of 1.3 ms. The atmosphere is effectively frozen on this timescale so that (in conditions of uniform scattering) the spectrum displays to a good approximation the instantaneous spatial variation of wind velocity through the lidar probe volume. A spectrum is produced only every 80–100 ms (corresponding to a duty cycle of 1.5%) because of dead time while the FFT calculations are being performed. We have since implemented faster processing that achieves 100% duty cycle, allowing significantly improved sensitivity through improved averaging. The signal strength in the upper trace corresponds to an atmospheric backscatter coefficient b(p)5 107 m1 sr1. The noise floor in the lidar spectra is governed by shot noise generated via detection of the local oscillator beam (Harris et al., 2001). The width of the spectrum gives an indication of turbulence levels within the 740 m probe length interrogated by the lidar. If the flow were completely uniform, then a dominant fraction of the spectrum would be concentrated in one or two bins of the FFT (Fig. 1a). A broadened spectrum (Fig. 1b), on the other hand, implies that the wind speed exhibits significant variation along the probe length. Note the small shoulder on the lowvelocity side of Fig. 1a. This feature is always seen with narrow spectra at high SNR and is caused by slowing down of the air as it approaches the turbine blades. The feature is quite small because significant slowing only occurs some distance from focus, where the lidar sensitivity is low.
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The wind speed was derived from such spectra by a simple peak-picking algorithm that identifies the spectral bin containing the greatest power spectral density. The Doppler frequency shift is then converted to velocity by multiplying by the conversion factor l/2, or
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0.775 m s1 per MHz. For narrow spectra such as that in Fig. 1a, the peak-picking process gives rise to minimal uncertainty. Larger errors are likely when the flow is more turbulent, as in Fig. 1b, though these can be reduced by calculating a running average. The data shown below are a running average of six successive measurements, corresponding to a timescale of order 1 s. Finally, the lower trace (Fig. 1c) has been acquired while one of the turbine blades is blocking the lidar beam: the very strong signal at low frequency is easily distinguished from wind data, and such spectra are ignored in the subsequent analysis. 3. Results Data were acquired successfully over the full period of deployment, but in order to illustrate the lidar capability we concentrate here on an 18-h period when disturbance of the flow caused by adjacent turbines should be negligible. The wind speed data are stored in files each of 1 h duration, and a selection of the output is displayed in Fig. 2. The data show a striking increase in the level of turbulence, although the mean wind speed remains broadly similar throughout. During the evening (19:00–20:00) the airflow is remarkably stable and uniform, with fluctuations increasing through the night, and becoming significantly more dramatic after sunrise (08:00 onwards). More detail of the wind speed fluctuations is revealed in Fig. 3, in which 10-min sections of the data from Fig. 2 are plotted. The top plot shows virtually no change in wind speed over the entire trace. In the next two plots, however, gusts are evident as sudden small (1 m s1) rises in speed occurring over just a few seconds, followed by a more gradual decline over a period of 20–40 s. By 08:40 (4th trace), the gusts are much larger and are separated by 1–2 min. The final two traces show further near-discontinuities in wind speed denoting the arrival of gusts, superimposed on a higher level of overall fluctuation. Fig. 4 looks in more detail at a good example of a gust. In Fig. 3, this can be seen as a sudden jump in speed at 08:44. In Fig. 4b, the gust is passing the beam focus at 200 m (corresponding to the mid-point of the lidar probe region), so that the two speeds contribute equally to the overall spectrum. The upper trace (Fig. 4a), obtained 5 s earlier, is dominated by the lower speed, while 7 s later the lower trace (Fig. 4c) shows virtually all the air in the probe volume now to be moving at the higher velocity. The roughly 10-s timescale for the transition depicted in Fig. 4 is consistent with the expected transit time of the gust through the lidar probe volume (80 m divided by 7 m s1). There are many other gust events in the measurement sequence comparable to that displayed in Fig. 4; however, a full and detailed interpretation of these events is difficult since the wind field is being probed only along a single line, and hence we have no information on the transverse structure of the gusts. We have considered other possible explanations for the observed near-discontinuity; for example, it would be possible to obtain an apparent rapid change in velocity if the lidar receives intermittent spurious returns from strongly scattering distant cloud banks. However, no such cloud banks were evident during the measurement period, and the fact that the area under the spectra in Fig. 4 remains constant to 30% indicates that backscatter levels are not fluctuating dramatically. We conclude it is difficult to find a plausible alternative explanation to the one proposed here of a genuine sudden rise in wind speed. The association of the gusts with the onset of solar ground heating may give some clues to their origin. A possible explanation is that the gusts are coherent structures produced in a strongly stratified surface layer, and triggered by a Kelvin–Helmholtz instability. A jet
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from above may penetrate down to a denser layer and create a front by pushing up a wall of denser air in front. More observations could be made employing multiple or scanning lidar systems to map the spatial behaviour, as a means to verify this explanation.
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Fig. 4. Evolution of Doppler spectra through a gust, with the time indicated on each plot. Plot (b) corresponds to the 44-min mark of the 4th trace in Fig. 3, where a near-discontinuity in wind speed from 4 to 7 m s1 is evident.
4. Comparison of lidar with sodar Lidar offers an alternative to sodar for remote anemometry, and it is useful to assess the relative merits and drawbacks of the two techniques in the specific application discussed here. Sodar involves the emission of sound pulses, and relies on the detection of the weak
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echo scattered from air density fluctuations (i.e. small-scale turbulence) (Antoniou et al., 2003). It measures the wind velocity via the Doppler shift of the acoustic pulses in a manner analogous to lidar. The velocity resolution and signal-to-noise ratio for lidar are typically much higher than for sodar, giving fast measurement that allows direct observation of turbulence or rapidly changing wind speed behaviour such as that presented here. Lidar does occasionally suffer from insufficient signal levels when the air is very clear, but such occurrences are rare within the PBL. On the other hand, sodar can experience problems when conditions are too calm or uniform, as there is insufficient turbulence to scatter the sound pulse. Also, if conditions are too windy then this generates background acoustic noise that can obscure the sodar signal. The spatial averaging inherent to sodar is seen as an advantage over point sensors such as cup and sonic anemometers; this advantage also applies to lidar. The maximum range for sodar is superior to the CW lidar described here, but not as good as high-energy pulsed lidar (Pearson and Collier, 1999). An assumption must also be made of uniform flow at each height, as for conically scanned lidar (Banakh et al., 1993); however for sodar, unlike lidar, it is not usually possible to perform a consistency check of this assumption. Rain can create problems for both sets of equipment; it generates acoustic noise that can obscure the sodar signal (Antoniou et al., 2003), whereas for lidar it can give a return that dominates aerosol scattering and hence is in error by inclusion of the rain’s downward motion under gravity. Cloud and rain are unlikely to lead to significant errors in the horizontal orientation employed here. Further errors in lidar data are possible when the light is scattered from moving objects such as birds or insects. It is assumed that such occurrences are rare and easily identified. Lidar is less likely than sodar to be restricted by environmental factors in the field, and this may lead to greater deployment flexibility. These factors include sodar’s acoustic emission, which restricts operation in residential areas, and its sensitivity to high background noise levels (e.g. near roads or airports). The wavelength of sound in sodar (cm) inevitably leads to side lobes on the emitted sound beam, which can hit trees, buildings, etc. and generate spurious signals. Lidar is immune to such effects as long as the beam does not directly strike moving objects. Overall, taking into account the considerations listed above, it is likely that lidar is much better suited to turbine-mounted deployment for the measurement of wind speed in advance of the blades. The acoustic background in such a location is likely to disturb the measurement, and side lobes will inevitably strike solid objects (blades, the ground, etc.). 5. Conclusions and future work The recent lidar developments reported here demonstrate considerable potential in wind power and meteorological applications, offering reliable near-turnkey operation. The lidar described has subsequently been reconfigured for conical scan operation, allowing groundbased measurement of wind profiles up to a height of 150 m for a detailed assessment of the wind resource (Smith et al., 2006). An improved version of this system (called ZephIR) is in production with 10 systems at present deployed in varied locations around the world. This paper reports the first time, to our knowledge, that a laser anemometer has been successfully mounted on the nacelle of a wind turbine for measurement of wind speed in front of the blades. Our principal aim here is the demonstration of lidar capability and so we attempt here no further detailed analysis or explanation for the effects noted in the
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previous sections. More experiments and analysis would need to be performed to determine whether such behaviour is typical. Similar examples of gusts are seen in many other datasets from the trial, but we cannot be certain that these measurements were not influenced by the adjacent turbines. It would also have been desirable to conduct parallel measurements with a mast-mounted cup anemometer in order to verify lidar performance. However, it is worth noting that many comparisons of ZephIR systems have since been made against cup anemometers, showing an impressive level of agreement (Smith et al., 2006; Albers and Smith, 2005; Kindler et al., 2006) and providing confidence in the validity of the measurements reported here. A more sophisticated analysis could utilise spectral data such as that in Figs. 1 and 4 to extract more information on the wind field. Indeed, subject to an assumption of uniform scattering within the probe volume, it should be possible to correlate the time series behaviour of Figs. 2 and 3 with evolution of the corresponding wind spectra. Lidar offers the potential to perform improved future experiments to measure the spatial and temporal coherence of gusts, as well as monitor other parameters such as the turbulence intensity. The fast data rate would allow rapid scanning in both angle and focus (range) to be performed, so that a three-dimensional picture of the wind field can be built up. Such an experiment would provide greater knowledge of the vertical and horizontal structure and temporal evolution of the gust features reported here. Nacelle-mounted lidar also has the potential to contribute to studies related to turbine performance, including wake behaviour and shadowing, velocity versus range, and power curve calibration (Manwell et al., 2002). In the only such example of which we are aware, a modified ZephIR lidar system was installed in a rearward-looking configuration on a test turbine at the Roskilde base of Risø in Denmark (Bingo¨l, 2005). A programmable scanner permitted examination of turbine wake behaviour in space and time and the deficit in wind speed associated with the wake was clearly observed. Acknowledgements We wish to acknowledge the contributions of many colleagues at QinetiQ Malvern to the work presented here. We also thank the Nordex technicians for their assistance in mounting the lidar on the nacelle, and the referees for their suggested explanations of the gust phenomena. References Albers, A., Smith, D., 2005. Den Wind mit Lasern messen. Erneuerbare Energien 4, 36–38. Antoniou, I., Jørgenson, H.E., Ormel, F., Bradley, S., von Hu¨nerbein, S., Emeis, S., Warmbier, G., 2003. On the theory of SODAR measurement techniques, Risø-R-1410(EN). Banakh, V.A., Smalikho, I.N., Ko¨pp, F., Werner, C., 1993. Representativeness of wind measurements with a cw Doppler lidar in the atmospheric boundary layer. Appl. Opt. 34, 2055–2067. Bingo¨l, F., 2005. Adapting a Doppler Laser anemometer to wind energy. Masters Thesis online /http:// www.afm.dtu.dk/Publications/msc.htmlS. Harris, M., Constant, G., Ward, C., 2001. Continuous-wave bistatic laser Doppler wind sensor. Appl. Opt. 40, 1501–1506. Harris, M., Young, R.I., Ko¨pp, F., Dolfi, A., Cariou, J.-P., 2002. Wake vortex detection and monitoring. Aerospace Sci. Technol. 6, 325–331. Harrison, R.G., Uppal, J.S., Johnstone, A., Moloney, J.V., 1990. Evidence of chaotic stimulated Brillouin scattering in optical fibres. Phys. Rev. Lett. 65, 167–170.
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Karlsson, C., Olsson, F., Letalick, D., Harris, M., 2000. All-fibre multifunction continuous-wave 1.55 micron coherent laser radar for range, speed, vibration and wind measurements. Appl. Opt. 39, 3716–3726. Kindler, D., Oldroyd, A., MacAskill, A., Finch, D., 2006. An 8 month test campaign of the QinetiQ ZephIR system: preliminary results. In: Extended Abstracts of the International Symposium for the Advancement of Boundary Layer Remote Sensing (ISARS 13), 18–20 July 2006, Garmisch-Partenkirchen, Germany, pp. 165–167. Manwell, J.F., McGowan, J.G., Rogers, A.L., 2002. Wind Energy Explained: Theory, Design and Application. Wiley, Chichester, UK. Pearson, G.N., Collier, C.G., 1999. A pulsed coherent CO2 lidar for boundary-layer meteorology. Q. J. R. Meteorol. Soc. 125, 2703–2721. Smith, D.A., Harris, M., Coffey, A.S., Mikkelsen, T., Jørgensen, H.E., Mann, J., Danielian, R., 2006. Wind lidar evaluation at the Danish wind test site in Høvsøre. Wind Energy 9, 87–93. Vaughan, J.M., Steinvall, K.O., Werner, C., Flamant, P.H., 1996. Coherent laser radar in Europe. Proc. IEEE 84, 205.
Journal of Wind Engineering and Industrial Aerodynamics 95 (2007) 1637–1647 www.elsevier.com/locate/jweia
Advance measurement of gusts by laser anemometry Michael Harrisa,, David J. Brycea, Adrian S. Coffeya, David A. Smitha, Jochen Birkemeyerb, Ullrich Knopf b a
Sensors and Electronics Division, QinetiQ Malvern, Worcestershire WR14 3PS, UK b Nordex Energy GmbH, Bornbarch 2, 22848 Norderstedt, Germany
Received 22 June 2004; received in revised form 8 December 2006; accepted 23 February 2007 Available online 11 April 2007
Abstract A coherent laser radar (or Doppler lidar) operating at a wavelength of 1.55 mm has been mounted on the nacelle of a 2.3 MW wind turbine in order to measure the wind speed in front of the blades at ranges up to 200 m. The lidar measures the component of wind speed along its beam direction but, since it rotates with the nacelle to point into the wind, this component normally differs negligibly from the actual wind speed. As an example to demonstrate the lidar’s capabilities, we present samples of wind data gathered over an 18 h period in March 2003, illustrating the development of gusts that appears to be associated with the onset of solar ground heating. Wind speeds over the full period lay in the range 4–10 m s1, and the gusts were evident as a near-discontinuity in wind speed typically of order 1–2 m s1, but reaching values larger than 3 m s1. The typical timescale between gusts was of order 1 min. Improvements to this experiment are proposed that will provide more detailed information on spatial and temporal structure of such gusts. r 2007 Elsevier Ltd. All rights reserved. Keywords: Coherent laser radar; Doppler lidar; Laser anemometry; Gusts; Turbulence
1. Introduction Coherent laser radar (CLR) or Doppler lidar is a technique that involves the emission of a coherent light beam, and detection of the weak return reflected or scattered from a distant target. The technique provides a means to measure the line-of-sight component of Corresponding author. Tel.: +44 1684 895731; fax: +44 1684 896270.
E-mail address: [email protected] (M. Harris). 0167-6105/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.jweia.2007.02.029
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wind speed via detection of the Doppler shift for light backscattered from natural aerosols (particles of dust, pollen, droplets, etc.) in the atmosphere (Vaughan et al., 1996). CLR was first developed in the 1970s using CO2 lasers, and since then has been used primarily as a research tool in applications such as the study of aircraft wake vortices (Harris et al., 2002). Recently, CLR systems based on comparatively cheap off-the-shelf telecommunications equipment operating at a wavelength l of 1.55 mm have been built (Karlsson et al., 2000; Harris et al., 2001) with the potential to offer routine autonomous remote wind speed measurement. We describe measurements made with one such system mounted on the nacelle of a Nordex N90 wind turbine at Postlow, 5 km W of Anklam, N.E. Germany. The aims of this paper are to demonstrate the capabilities of lidar by presenting some selected examples of wind speed data showing interesting behaviour over a 1-day period in March 2003, and hence to alert the wind engineering community to the potential of lidar as a reliable and convenient method for remote anemometry. The data show uniform stable airflow during the early part of the night, followed by the development of a pattern of distinctive repeating gusts. Further experiments are suggested that would provide significantly improved information on the nature of the gusts. Finally, a brief comparison is made of the capabilities and limitations of lidar for anemometry compared with that of more widely deployed sodar techniques. 2. Experiment The lidar is a continuous-wave (CW) monostatic design, capable of measuring line-ofsight wind speeds up to 39 m s1. The basic principles of its operation are found in Karlsson et al. (2000), with more details of this particular system given in Smith et al. (2006). The lidar transceiver was mounted on top of the nacelle of a Nordex N90 wind turbine, and was connected by fibre-optic and electrical cable to a control unit (in the base of the tower) containing the laser source, detector and signal-processing computer. The transmitted laser power would normally be 1 W; however, with the long (120 m) connecting fibres used here the output was restricted to 400 mW in order to avoid the non-linear optical mechanism of stimulated Brillouin scattering (Harrison et al., 1990), which would lead to excessive noise and poor lidar performance. The system could be focused at any range R from 10 m out to 200 m, and it measured a value for wind speed approximately 10 times every second. For the data presented in the next section, the lidar focus was fixed at 200 m. At this setting, the lidar probe volume (a pencil-thin region of space, diameter 10 mm at focus) is of considerable extent, with the relative sensitivity S as a function of range z given to a good approximation by SðzÞ /
L , ðz RÞ2 þ L2
(1)
where R is the range to beam focus and L is the characteristic (Rayleigh) length for the beam to double its area on either side of the focal plane lR2 , (2) pA2 where A is the transmitted beam radius (to 1/e2 intensity). As used here, the lidar sensitivity is highest at R ¼ 200 m, and decays away from the focus as a Lorentzian function with a half-width of L40 m. L
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The terrain around the site is very flat for a considerable distance, consisting of farmland with no large buildings and occasional lines of trees. The lidar beam was aligned roughly parallel to the rotor axis, propagating horizontally at a height 90 m above mean ground level. The system remained installed for a three-week period in March 2003; here we report specifically a series of measurements from a limited period over 4–5 March, during which the wind remained roughly East-South-Easterly. The geometry of the site ensures that under such conditions our wind measurements were not influenced by the presence of other wind turbines situated over 250 m away to the N and S. Because the lidar only measures the line-of-sight component of the wind speed, we relied upon rotation of the nacelle to ensure that the lidar beam always pointed close to the upwind direction. The error from imperfect alignment with wind direction is very small, and is easily derived from the cosine of the angular error (giving, for example, an underestimate of wind speed by 1.5% for 101 of misalignment). Because the lidar beam points forwards from the back of the nacelle, it is blocked approximately once per second by the rotating blades of the turbine. As a result, only 60% of measurements are successful with the beam striking a blade in the remaining 40%. Such occurrences give rise to very strong signals at low frequency (blade motion is nearly orthogonal to the lidar beam, so the Doppler shift is small) and are easily identified and rejected. The detector requires no recovery time from the intense blade reflection, so that blocking presents no real problem in practice, with the lidar obtaining five or six independent wind measurements every second. Weather conditions were stable for the period of measurements presented here, with a consistent ESE wind of mean value 5–7 m s1. The temperature remained close to freezing, with lying snow and night frost. There was little cloud cover with hazy sunshine in the daytime, and visibility was estimated to be 5–10 km giving good uniform scattering that resulted in high signal-to-noise ratio (SNR) for the lidar wind measurements. Fig. 1 shows typical wind Doppler spectra, acquired via a 512-point fast Fourier transform (FFT) process. The raw data-sampling rate is 100 M samples s1, and power spectra derived from 256 individual FFTs are averaged to produce each spectrum, which represents a measurement time of 1.3 ms. The atmosphere is effectively frozen on this timescale so that (in conditions of uniform scattering) the spectrum displays to a good approximation the instantaneous spatial variation of wind velocity through the lidar probe volume. A spectrum is produced only every 80–100 ms (corresponding to a duty cycle of 1.5%) because of dead time while the FFT calculations are being performed. We have since implemented faster processing that achieves 100% duty cycle, allowing significantly improved sensitivity through improved averaging. The signal strength in the upper trace corresponds to an atmospheric backscatter coefficient b(p)5 107 m1 sr1. The noise floor in the lidar spectra is governed by shot noise generated via detection of the local oscillator beam (Harris et al., 2001). The width of the spectrum gives an indication of turbulence levels within the 740 m probe length interrogated by the lidar. If the flow were completely uniform, then a dominant fraction of the spectrum would be concentrated in one or two bins of the FFT (Fig. 1a). A broadened spectrum (Fig. 1b), on the other hand, implies that the wind speed exhibits significant variation along the probe length. Note the small shoulder on the lowvelocity side of Fig. 1a. This feature is always seen with narrow spectra at high SNR and is caused by slowing down of the air as it approaches the turbine blades. The feature is quite small because significant slowing only occurs some distance from focus, where the lidar sensitivity is low.
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The wind speed was derived from such spectra by a simple peak-picking algorithm that identifies the spectral bin containing the greatest power spectral density. The Doppler frequency shift is then converted to velocity by multiplying by the conversion factor l/2, or
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0.775 m s1 per MHz. For narrow spectra such as that in Fig. 1a, the peak-picking process gives rise to minimal uncertainty. Larger errors are likely when the flow is more turbulent, as in Fig. 1b, though these can be reduced by calculating a running average. The data shown below are a running average of six successive measurements, corresponding to a timescale of order 1 s. Finally, the lower trace (Fig. 1c) has been acquired while one of the turbine blades is blocking the lidar beam: the very strong signal at low frequency is easily distinguished from wind data, and such spectra are ignored in the subsequent analysis. 3. Results Data were acquired successfully over the full period of deployment, but in order to illustrate the lidar capability we concentrate here on an 18-h period when disturbance of the flow caused by adjacent turbines should be negligible. The wind speed data are stored in files each of 1 h duration, and a selection of the output is displayed in Fig. 2. The data show a striking increase in the level of turbulence, although the mean wind speed remains broadly similar throughout. During the evening (19:00–20:00) the airflow is remarkably stable and uniform, with fluctuations increasing through the night, and becoming significantly more dramatic after sunrise (08:00 onwards). More detail of the wind speed fluctuations is revealed in Fig. 3, in which 10-min sections of the data from Fig. 2 are plotted. The top plot shows virtually no change in wind speed over the entire trace. In the next two plots, however, gusts are evident as sudden small (1 m s1) rises in speed occurring over just a few seconds, followed by a more gradual decline over a period of 20–40 s. By 08:40 (4th trace), the gusts are much larger and are separated by 1–2 min. The final two traces show further near-discontinuities in wind speed denoting the arrival of gusts, superimposed on a higher level of overall fluctuation. Fig. 4 looks in more detail at a good example of a gust. In Fig. 3, this can be seen as a sudden jump in speed at 08:44. In Fig. 4b, the gust is passing the beam focus at 200 m (corresponding to the mid-point of the lidar probe region), so that the two speeds contribute equally to the overall spectrum. The upper trace (Fig. 4a), obtained 5 s earlier, is dominated by the lower speed, while 7 s later the lower trace (Fig. 4c) shows virtually all the air in the probe volume now to be moving at the higher velocity. The roughly 10-s timescale for the transition depicted in Fig. 4 is consistent with the expected transit time of the gust through the lidar probe volume (80 m divided by 7 m s1). There are many other gust events in the measurement sequence comparable to that displayed in Fig. 4; however, a full and detailed interpretation of these events is difficult since the wind field is being probed only along a single line, and hence we have no information on the transverse structure of the gusts. We have considered other possible explanations for the observed near-discontinuity; for example, it would be possible to obtain an apparent rapid change in velocity if the lidar receives intermittent spurious returns from strongly scattering distant cloud banks. However, no such cloud banks were evident during the measurement period, and the fact that the area under the spectra in Fig. 4 remains constant to 30% indicates that backscatter levels are not fluctuating dramatically. We conclude it is difficult to find a plausible alternative explanation to the one proposed here of a genuine sudden rise in wind speed. The association of the gusts with the onset of solar ground heating may give some clues to their origin. A possible explanation is that the gusts are coherent structures produced in a strongly stratified surface layer, and triggered by a Kelvin–Helmholtz instability. A jet
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from above may penetrate down to a denser layer and create a front by pushing up a wall of denser air in front. More observations could be made employing multiple or scanning lidar systems to map the spatial behaviour, as a means to verify this explanation.
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4. Comparison of lidar with sodar Lidar offers an alternative to sodar for remote anemometry, and it is useful to assess the relative merits and drawbacks of the two techniques in the specific application discussed here. Sodar involves the emission of sound pulses, and relies on the detection of the weak
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echo scattered from air density fluctuations (i.e. small-scale turbulence) (Antoniou et al., 2003). It measures the wind velocity via the Doppler shift of the acoustic pulses in a manner analogous to lidar. The velocity resolution and signal-to-noise ratio for lidar are typically much higher than for sodar, giving fast measurement that allows direct observation of turbulence or rapidly changing wind speed behaviour such as that presented here. Lidar does occasionally suffer from insufficient signal levels when the air is very clear, but such occurrences are rare within the PBL. On the other hand, sodar can experience problems when conditions are too calm or uniform, as there is insufficient turbulence to scatter the sound pulse. Also, if conditions are too windy then this generates background acoustic noise that can obscure the sodar signal. The spatial averaging inherent to sodar is seen as an advantage over point sensors such as cup and sonic anemometers; this advantage also applies to lidar. The maximum range for sodar is superior to the CW lidar described here, but not as good as high-energy pulsed lidar (Pearson and Collier, 1999). An assumption must also be made of uniform flow at each height, as for conically scanned lidar (Banakh et al., 1993); however for sodar, unlike lidar, it is not usually possible to perform a consistency check of this assumption. Rain can create problems for both sets of equipment; it generates acoustic noise that can obscure the sodar signal (Antoniou et al., 2003), whereas for lidar it can give a return that dominates aerosol scattering and hence is in error by inclusion of the rain’s downward motion under gravity. Cloud and rain are unlikely to lead to significant errors in the horizontal orientation employed here. Further errors in lidar data are possible when the light is scattered from moving objects such as birds or insects. It is assumed that such occurrences are rare and easily identified. Lidar is less likely than sodar to be restricted by environmental factors in the field, and this may lead to greater deployment flexibility. These factors include sodar’s acoustic emission, which restricts operation in residential areas, and its sensitivity to high background noise levels (e.g. near roads or airports). The wavelength of sound in sodar (cm) inevitably leads to side lobes on the emitted sound beam, which can hit trees, buildings, etc. and generate spurious signals. Lidar is immune to such effects as long as the beam does not directly strike moving objects. Overall, taking into account the considerations listed above, it is likely that lidar is much better suited to turbine-mounted deployment for the measurement of wind speed in advance of the blades. The acoustic background in such a location is likely to disturb the measurement, and side lobes will inevitably strike solid objects (blades, the ground, etc.). 5. Conclusions and future work The recent lidar developments reported here demonstrate considerable potential in wind power and meteorological applications, offering reliable near-turnkey operation. The lidar described has subsequently been reconfigured for conical scan operation, allowing groundbased measurement of wind profiles up to a height of 150 m for a detailed assessment of the wind resource (Smith et al., 2006). An improved version of this system (called ZephIR) is in production with 10 systems at present deployed in varied locations around the world. This paper reports the first time, to our knowledge, that a laser anemometer has been successfully mounted on the nacelle of a wind turbine for measurement of wind speed in front of the blades. Our principal aim here is the demonstration of lidar capability and so we attempt here no further detailed analysis or explanation for the effects noted in the
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previous sections. More experiments and analysis would need to be performed to determine whether such behaviour is typical. Similar examples of gusts are seen in many other datasets from the trial, but we cannot be certain that these measurements were not influenced by the adjacent turbines. It would also have been desirable to conduct parallel measurements with a mast-mounted cup anemometer in order to verify lidar performance. However, it is worth noting that many comparisons of ZephIR systems have since been made against cup anemometers, showing an impressive level of agreement (Smith et al., 2006; Albers and Smith, 2005; Kindler et al., 2006) and providing confidence in the validity of the measurements reported here. A more sophisticated analysis could utilise spectral data such as that in Figs. 1 and 4 to extract more information on the wind field. Indeed, subject to an assumption of uniform scattering within the probe volume, it should be possible to correlate the time series behaviour of Figs. 2 and 3 with evolution of the corresponding wind spectra. Lidar offers the potential to perform improved future experiments to measure the spatial and temporal coherence of gusts, as well as monitor other parameters such as the turbulence intensity. The fast data rate would allow rapid scanning in both angle and focus (range) to be performed, so that a three-dimensional picture of the wind field can be built up. Such an experiment would provide greater knowledge of the vertical and horizontal structure and temporal evolution of the gust features reported here. Nacelle-mounted lidar also has the potential to contribute to studies related to turbine performance, including wake behaviour and shadowing, velocity versus range, and power curve calibration (Manwell et al., 2002). In the only such example of which we are aware, a modified ZephIR lidar system was installed in a rearward-looking configuration on a test turbine at the Roskilde base of Risø in Denmark (Bingo¨l, 2005). A programmable scanner permitted examination of turbine wake behaviour in space and time and the deficit in wind speed associated with the wake was clearly observed. Acknowledgements We wish to acknowledge the contributions of many colleagues at QinetiQ Malvern to the work presented here. We also thank the Nordex technicians for their assistance in mounting the lidar on the nacelle, and the referees for their suggested explanations of the gust phenomena. References Albers, A., Smith, D., 2005. Den Wind mit Lasern messen. Erneuerbare Energien 4, 36–38. Antoniou, I., Jørgenson, H.E., Ormel, F., Bradley, S., von Hu¨nerbein, S., Emeis, S., Warmbier, G., 2003. On the theory of SODAR measurement techniques, Risø-R-1410(EN). Banakh, V.A., Smalikho, I.N., Ko¨pp, F., Werner, C., 1993. Representativeness of wind measurements with a cw Doppler lidar in the atmospheric boundary layer. Appl. Opt. 34, 2055–2067. Bingo¨l, F., 2005. Adapting a Doppler Laser anemometer to wind energy. Masters Thesis online /http:// www.afm.dtu.dk/Publications/msc.htmlS. Harris, M., Constant, G., Ward, C., 2001. Continuous-wave bistatic laser Doppler wind sensor. Appl. Opt. 40, 1501–1506. Harris, M., Young, R.I., Ko¨pp, F., Dolfi, A., Cariou, J.-P., 2002. Wake vortex detection and monitoring. Aerospace Sci. Technol. 6, 325–331. Harrison, R.G., Uppal, J.S., Johnstone, A., Moloney, J.V., 1990. Evidence of chaotic stimulated Brillouin scattering in optical fibres. Phys. Rev. Lett. 65, 167–170.
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Karlsson, C., Olsson, F., Letalick, D., Harris, M., 2000. All-fibre multifunction continuous-wave 1.55 micron coherent laser radar for range, speed, vibration and wind measurements. Appl. Opt. 39, 3716–3726. Kindler, D., Oldroyd, A., MacAskill, A., Finch, D., 2006. An 8 month test campaign of the QinetiQ ZephIR system: preliminary results. In: Extended Abstracts of the International Symposium for the Advancement of Boundary Layer Remote Sensing (ISARS 13), 18–20 July 2006, Garmisch-Partenkirchen, Germany, pp. 165–167. Manwell, J.F., McGowan, J.G., Rogers, A.L., 2002. Wind Energy Explained: Theory, Design and Application. Wiley, Chichester, UK. Pearson, G.N., Collier, C.G., 1999. A pulsed coherent CO2 lidar for boundary-layer meteorology. Q. J. R. Meteorol. Soc. 125, 2703–2721. Smith, D.A., Harris, M., Coffey, A.S., Mikkelsen, T., Jørgensen, H.E., Mann, J., Danielian, R., 2006. Wind lidar evaluation at the Danish wind test site in Høvsøre. Wind Energy 9, 87–93. Vaughan, J.M., Steinvall, K.O., Werner, C., Flamant, P.H., 1996. Coherent laser radar in Europe. Proc. IEEE 84, 205.