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J. F. Newman; A. Clifton (2017)
Publisher: Copernicus Publications
Journal: Wind Energy Science
Languages: English
Types: Article
Subjects: TJ807-830, Renewable energy sources
Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry.

In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices.

L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physics-based corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI) error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • ARM (Atmospheric Radiation Measurement): Climate Research Facility, updated daily, Facility-specific multi-level meteorological instrumentation (TWR), Nov. 2012-Jun. 2013, 36 36018.000 N, 97 2906.000 W: Southern Great Plains Central Facility (C1), compiled by: Cook, D. and Kyrouac, J., ARM Data Archive: Oak Ridge, Tennessee, USA, available at: https://www.arm.gov/capabilities/instruments/twr (last access: 11 April 2013), 1993.
    • ARM (Atmospheric Radiation Measurement): Climate Research Facility, updated daily, Carbon dioxide flux measurement systems (CO2FLX), Nov. 2012-Jun. 2013, 36 36018.000 N, 97 2906.000 W: Southern Great Plains Central Facility (C1), compiled by: Billesbach, D., Biraud, S., and Chan, S., ARM Data Archive: Oak Ridge, Tennessee, USA, available at: https://www.arm.gov/capabilities/instruments/co2flx (last access: 11 April 2013), 2011.
    • Arya, S. P.: Introduction to Micrometeorology, Academic Press, Cornwall, UK, 2nd Edn., Int. Geophys. Ser., 79, 101-108, 2001.
    • Barthelmie, R. J., Crippa, P., Wang, H., Smith, C. M., Krishnamurthy, R., Choukulkar, A., Calhoun, R., Valyou, D., Marzocca, P., Matthiesen, D., Brown, G., and Pryor, S. C.: 3D wind and turbulence characteristics of the atmospheric boundary layer, Bull. Amer. Meteor. Soc., 95, 743-756, doi:10.1175/BAMS-D12-00111.1, 2013.
    • Bodine, D., Klein, P. M., Arms, S. C., and Shapiro, A.: Variability of surface air temperature over gently sloped terrain, J. Appl. Meteor. Climatol., 48, 1117-1141, 2009.
    • Boquet, M., Callard, P., Deve, N., and Osler, E.: Return on investment of a lidar remote sensing device, DEWI Magazine, 37, 56- 61, 2010.
    • Branlard, E., Pedersen, A. T., Mann, J., Angelou, N., Fischer, A., Mikkelsen, T., Harris, M., Slinger, C., and Montes, B. F.: Retrieving wind statistics from average spectrum of continuouswave lidar, Atmos. Meas. Tech., 6, 1673-1683, doi:10.5194/amt6-1673-2013, 2013.
    • Browning, K. A. and Wexler, R.: The determination of kinematic properties of a wind field using Doppler radar, J. Appl. Meteor., 7, 105-113, doi:10.1175/1520- 0450(1968)007<0105:TDOKPO>2.0.CO;2, 1968.
    • Bulaevskaya, V., Wharton, S., Clifton, A., Qualley, G., and Miller, W. O.: Wind power curve modeling in complex terrain using statistical models, Journal of Renewable and Sustainable Energy, 7, 013103, doi:10.1063/1.4904430, 2015.
    • Burton, T., Sharpe, D., Jenkins, N., and Bossanyi, E.: Wind Energy Handbook, John Wiley & Sons, Ltd., 2001.
    • Calhoun, R., Heap, R., Princevac, M., Newsom, R., Fernando, H., and Ligon, D.: Virtual towers using coherent Doppler lidar during the Joint Urban 2003 Dispersion Experiment, J. Appl. Meteor., 45, 1116-1126, doi:10.1175/JAM2391.1, 2006.
    • Chang, W. S.: Principles of Lasers and Optics, Cambridge University Press, 2005.
    • Clifton, A. and Wagner, R.: Accounting for the effect of turbulence on wind turbine power curves, J. Phys. Conf. Ser., 524, 012109, doi:10.1088/1742-6596/524/1/012109, 2014.
    • Clifton, A., Kilcher, L., Lundquist, J. K., and Fleming, P.: Using machine learning to predict wind turbine power output, Environ. Res. Lett., 8, 024009, doi:10.1088/1748-9326/8/2/024009, 2013.
    • Clifton, A., Boquet, M., Roziers, E. B. D., Westerhellweg, A., Hofsäß, M., Klaas, T., Vogstad, K., Clive, P., Harris, M., Wylie, S., Osler, E., Banta, B., Choukulkar, A., Lundquist, J., and Aitken, M.: Remote sensing of complex flows by Doppler wind lidar: Issues and preliminary recommendations, Tech. Rep. NREL/TP5000-64634, NREL, http://www.nrel.gov/docs/fy16osti/64634. pdf (last access: 3 February 2017), 2015.
    • Efron, B. and Gong, G.: A leisurely look at the bootstrap, the jackknife, and cross-validation, Am. Stat., 37, 36-48, 1983.
    • Elliott, D. L. and Cadogan, J. B.: Effects of wind shear and turbulence on wind turbine power curves, in: European Community Wind Energy Conference and Exhibition, Madrid, Spain, 1990.
    • Emeis, S.: Measurement Methods in Atmospheric Sciences: In-Situ and Remote, Borntraeger Science Publishers, 257 pp., 2010.
    • Friedman, J., Hastie, T., and Tibshirani, R.: The Elements of Statistical Learning, Springer Series in Statistics Springer, Berlin, Vol. 1, 587-604, 2001.
    • Friedman, J. H.: Multivariate adaptive regression splines, Ann. Stat., 19, 1-67, 1991.
    • Fuertes, F. C., Iungo, G. V., and Porté-Agel, F.: 3D turbulence measurements using three synchronous wind lidars: Validation against sonic anemometry, J. Atmos. Ocean. Tech., 31, 1549- 1556, doi:10.1175/JTECH-D-13-00206.1, 2014.
    • Hogan, R. J., Grant, A. L., Illingworth, A. J., Pearson, G. N., and O'Connor, E. J.: Vertical velocity variance and skewness in clear and cloud-topped boundary layers as revealed by Doppler lidar, Q. J. Roy. Meteor. Soc., 135, 635-643, doi:10.1002/qj.413, 2009.
    • Huffaker, R. M. and Hardesty, R. M.: Remote sensing of atmospheric wind velocities using solid-state and CO2 coherent laser systems, P. IEEE, 84, 181-204, 1996.
    • International Electrotechnical Commission: Wind turbines - Part 1: Design requirements, Tech. Rep., IEC 61400-1, Geneva, Switzerland, 2005.
    • International Electrotechnical Commission: Wind turbines - Part 12-1: Power performance measurements of electricity producing wind turbines, Tech. Rep. Committee draft Edn., IEC 61400-12- 1, Geneva, Switzerland, 2013.
    • Kaimal, J. and Finnigan, J.: Atmospheric Boundary Layer Flows: Their Structure and Measurement, Oxford University Press, 1994.
    • Kolmogorov, A. N.: The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers, Doklady AN SSSR, 30, 301-304, 1941.
    • Krishnamurthy, R., Calhoun, R., Billings, B., and Doyle, J.: Wind turbulence estimates in a valley by coherent Doppler lidar, Meteorol. Appl., 18, 361-371, doi:10.1002/met.263, 2011.
    • Krishnamurthy, R., Choukulkar, A., Calhoun, R., Fine, J., Oliver, A., and Barr, K.: Coherent Doppler lidar for wind farm characterization, Wind Energy, 16, 189-206, doi:10.1002/we.539, 2013.
    • Lenschow, D. H., Wulfmeyer, V., and Senff, C.: Measuring second-through fourth-order moments in noisy data, J. Atmos. Ocean. Tech., 17, 1330-1347, doi:10.1175/1520- 0426(2000)017<1330:MSTFOM>2.0.CO;2, 2000.
    • Lundquist, J. K., Churchfield, M. J., Lee, S., and Clifton, A.: Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics, Atmos. Meas. Tech., 8, 907-920, doi:10.5194/amt-8-907- 2015, 2015.
    • Mann, J.: The spatial structure of neutral atmospheric surface-layer turbulence, J. Fluid Mech., 273, 141-168, doi:10.1017/S0022112094001886, 1994.
    • Mann, J., Peña, A., Bingöl, F., Wagner, R., and Courtney, M. S.: Lidar scanning of momentum flux in and above the atmospheric surface layer, J. Atmos. Ocean. Tech., 27, 959-976, doi:10.1175/2010JTECHA1389.1, 2010.
    • Mather, J. H. and Voyles, J. W.: The ARM Climate Research Facility: A review of structure and capabilities, Bull. Amer. Meteor. Soc., 94, 377-392, doi:10.1175/BAMS-D-11-00218.1, 2013.
    • Newman, J. F.: Optimizing lidar scanning strategies for wind energy turbulence measurements, Ph.D. thesis, University of Oklahoma, Norman, Oklahoma, USA, 2015.
    • Newman, J. F. and Klein, P. M.: The impacts of atmospheric stability on the accuracy of wind speed extrapolation methods, Resources, 3, 81-105, doi:10.3390/resources3010081, 2014.
    • Newman, J. F., Bonin, T. A., Klein, P. M., Wharton, S., and Newsom, R. K.: Testing and validation of multi-lidar scanning strategies for wind energy applications, Wind Energy, 19, 2239-2254, doi:10.1002/we.1978, 2016a.
    • Newman, J. F., Klein, P. M., Wharton, S., Sathe, A., Bonin, T. A., Chilson, P. B., and Muschinski, A.: Evaluation of three lidar scanning strategies for turbulence measurements, Atmos. Meas. Tech., 9, 1993-2013, doi:10.5194/amt-9-1993-2016, 2016b.
    • Newsom, R. K., Berg, L. K., Shaw, W. J., and Fischer, M. L.: Turbine-scale wind field measurements using dual-Doppler lidar, Wind Energy, 18, 219-235, doi:10.1002/we.1691, 2015.
    • Peinke, J., Barth, S., Böttcher, F., Heinemann, D., and Lange, B.: Turbulence, a challenging problem for wind energy, Physica A, 338, 187-193, 2004.
    • Peña, A., Hasager, C. B., Gryning, S.-E., Courtney, M., Antoniou, I., and Mikkelsen, T.: Offshore wind profiling using light detection and ranging measurements, Wind Energy, 12, 105-124, doi:10.1002/we.283, 2009.
    • Petersen, E. L., Mortensen, N. G., Landberg, L., Højstrup, J., and Frank, H. P.: Wind power meteorology, Part I: Climate and turbulence, Wind Energy, 1, 25-45, 1998.
    • Rodrigo, J. S., Guillén, F. B., Arranz, P. G., Courtney, M., Wagner, R., and Dupont, E.: Multi-site testing and evaluation of remote sensing instruments for wind energy applications, Renew. Energ., 53, 200-210, doi:10.1016/j.renene.2012.11.020, 2013.
    • Sathe, A. and Mann, J.: A review of turbulence measurements using ground-based wind lidars, Atmos. Meas. Tech., 6, 3147-3167, doi:10.5194/amt-6-3147-2013, 2013.
    • Sathe, A., Mann, J., Gottschall, J., and Courtney, M. S.: Can wind lidars measure turbulence?, J. Atmos. Ocean. Tech., 28, 853-868, doi:10.1175/JTECH-D-10-05004.1, 2011.
    • Sathe, A., Mann, J., Barlas, T., Bierbooms, W. A. A. M., and van Bussel, G. J. W.: Influence of atmospheric stability on wind turbine loads, Wind Energy, 16, 1013-1032, doi:10.1002/we.1528, 2013.
    • Sathe, A., Banta, R., Pauscher, L., Vogstad, K., Schlipf, D., and Wylie, S.: Estimating turbulence statistics and parameters from ground- and nacelle-based lidar measurements: IEA Wind expert report, DTU Wind Energy, Denmark, 2015a.
    • Sathe, A., Mann, J., Vasiljevic, N., and Lea, G.: A six-beam method to measure turbulence statistics using ground-based wind lidars, Atmos. Meas. Tech., 8, 729-740, doi:10.5194/amt-8-729-2015, 2015b.
    • Schneemann, J., Voss, S., Steinfeld, G., Trabucchi, D., Trujillo, J. J., Witha, B., and Kühn, M.: Lidar simulations to study measurements of turbulence in different atmospheric conditions, in: Wind Energy - Impact of Turbulence, Springer Berlin Heidelberg, Berlin, Heidelberg, 127-132, 2014.
    • Sjöholm, M., Mikkelsen, T., Mann, J., Enevoldsen, K., and Courtney, M.: Time series analysis of continuous-wave coherent Doppler lidar wind measurements, IOP C. Ser. Earth Env., 1, 012051, doi:10.1088/1755-1307/1/1/012051, 2008.
    • Sjöholm, M., Mikkelsen, T., Mann, J., Enevoldsen, K., and Courtney, M.: Spatial averaging-effects on turbulence measured by a continuous-wave coherent lidar, Meteor. Z., 18, 281-287, doi:10.1127/0941-2948/2009/0379, 2009.
    • Slinger, C. and Harris, M.: Introduction to continuous-wave Doppler lidar, in: Summer School in Remote Sensing for Wind Energy, Boulder, CO, 2012.
    • Sonnenschein, C. M. and Horrigan, F. A.: Signal-to-noise relationships for coaxial systems that heterodyne backscatter from the atmosphere, Appl. Opt., 10, 1600-1604, doi:10.1364/AO.10.001600, 1971.
    • Stawiarski, C., Träumner, K., Knigge, C., and Calhoun, R.: Scopes and challenges of dual-Doppler lidar wind measurements - An error analysis, J. Atmos. Ocean. Tech., 30, 2044-2062, doi:10.1175/JTECH-D-12-00244.1, 2013.
    • Strauch, R. G., Merritt, D. A., Moran, K. P., Earnshaw, K. B., and De Kamp, D. V.: The Colorado wind-profiling network, J. Atmos. Ocean. Tech., 1, 37-49, doi:10.1175/1520- 0426(1984)001<0037:TCWPN>2.0.CO;2, 1984.
    • Stull, R. B.: Meteorology for Scientists and Engineers, Brooks/Cole, 2nd Edn., 2000.
    • Vanderwende, B. J. and Lundquist, J. K.: The modification of wind turbine performance by statistically distinct atmospheric regimes, Environ. Res. Lett., 7, 034035, doi:10.1088/1748- 9326/7/3/034035, 2012.
    • Vasiljevic, N., Courtney, M., and Mann, J.: A time-space synchronization of coherent Doppler scanning lidars for 3D measurements of wind fields, PhD thesis, Denmark Technical University, Denmark, 2014.
    • Vickers, D. and Mahrt, L.: Quality control and flux sampling problems for tower and aircraft data, J. Atmos. Ocean. Tech., 14, 512-526, doi:10.1175/1520- 0426(1997)014<0512:QCAFSP>2.0.CO;2, 1997.
    • Wagner, R., Antoniou, I., Pedersen, S. M., Courtney, M. S., and Jørgensen, H. E.: The influence of the wind speed profile on wind turbine performance measurements, Wind Energy, 12, 348-362, doi:10.1002/we.297, 2009.
    • Wainwright, C. E., Stepanian, P. M., Chilson, P. B., Palmer, R. D., Fedorovich, E., and Gibbs, J. A.: A time series sodar simulator based on large-eddy simulation, J. Atmos. Ocean. Tech., 31, 876- 889, doi:10.1175/JTECH-D-13-00161.1, 2014.
    • Walter, K., Weiss, C. C., Swift, A. H., Chapman, J., and Kelley, N. D.: Speed and direction shear in the stable nocturnal boundary layer, J. Sol. Energ.-T. Asme, 131, 11013-11013, doi:10.1115/1.3035818, 2009.
    • Wang, H., Barthelmie, R. J., Clifton, A., and Pryor, S. C.: Wind measurements from arc scans with Doppler wind lidar, J. Atmos. Ocean. Tech., 32, 2024-2040, doi:10.1175/JTECH-D-14- 00059.1, 2015.
    • Weitkamp, C.: Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, Springer Series in Optical Sciences, Springer Science & Business Media, 102, 325-354, 2005.
    • Wharton, S. and Lundquist, J. K.: Assessing atmospheric stability and its impacts on rotor-disk wind characteristics at an onshore wind farm, Wind Energy, 15, 525-546, doi:10.1002/we.483, 2012.
    • Wharton, S., Newman, J. F., Qualley, G., and Miller, W. O.: Measuring turbine inflow with vertically-profiling lidar in complex terrain, J. Wind Eng. Ind. Aerodyn., 142, 217-231, doi:10.1016/j.jweia.2015.03.023, 2015.
    • Wyngaard, J. C.: The effects of probe-induced flow distortion on atmospheric turbulence measurements, J. Appl. Meteorol., 20, 784-794, 1981.
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