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http://dx.doi.org/10.12989/was.2011.14.4.337

Reconstruction of gusty wind speed time series from autonomous data logger records  

Amezcua, Javier (Department of Atmospheric and Oceanic Science University of Maryland)
Munoz, Raul (Physics Department, Instituto Tecnologico y de Estudios Superiores de Monterrey)
Probst, Oliver (Physics Department, Instituto Tecnologico y de Estudios Superiores de Monterrey)
Publication Information
Wind and Structures / v.14, no.4, 2011 , pp. 337-357 More about this Journal
Abstract
The collection of wind speed time series by means of digital data loggers occurs in many domains, including civil engineering, environmental sciences and wind turbine technology. Since averaging intervals are often significantly larger than typical system time scales, the information lost has to be recovered in order to reconstruct the true dynamics of the system. In the present work we present a simple algorithm capable of generating a real-time wind speed time series from data logger records containing the average, maximum, and minimum values of the wind speed in a fixed interval, as well as the standard deviation. The signal is generated from a generalized random Fourier series. The spectrum can be matched to any desired theoretical or measured frequency distribution. Extreme values are specified through a postprocessing step based on the concept of constrained simulation. Applications of the algorithm to 10-min wind speed records logged at a test site at 60 m height above the ground show that the recorded 10-min values can be reproduced by the simulated time series to a high degree of accuracy.
Keywords
wind speed; time series; gusts; kaimal distribution; constrained simulation; autocorrelation function;
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1 Aksoy, H., Toprak, Z.F., Aytek, A. and Unal, N.E. (2004), "Stochastic generation of hourly mean wind speed data", Renew. Energ., 29(14), 2111-2131.   DOI   ScienceOn
2 Audierne, E., Bergami, L., Elizondo, J. and Probst, O. (2010), "Analysis of the furling behavior of small wind turbines", Appl. Energ., 87(7), 2278-2292.   DOI   ScienceOn
3 Brown, B.G., Katz, R.W. and Murphy, A.H. (1984), "Time series models to simulate and forecast wind speed and wind power", J. Appl. Meteorol. Clim., 23, 1184-1195.   DOI   ScienceOn
4 Bierbooms, W. and Cheng, Po-Wen. (2002), "Stochastic gust model for design calculations of wind turbines", J. Wind Eng. Ind. Aerod., 90(11), 1237-1251.   DOI   ScienceOn
5 Bierbooms, W. (2005), "Investigation of spatial gusts with extreme rise time on the extreme loads of pitchregulated wind turbines", Wind Energy, 8(1), 17-34.   DOI   ScienceOn
6 Burton, T., Sharpe, D., Jenkins, N. and Bossanyi, E. (2001), Wind Energy Handbook., John Wiley & Sons, United Kingdom.
7 Cadenas, E. and Rivera, W. (2007), "Wind speed forecasting in the South Coast of Oaxaca, Mexico", Renew. Energ., 32(12), 2116-2128.   DOI   ScienceOn
8 Childers, D. (1997), Probability and random processes using Matlab with applications to continuous and discrete time series. Irwin/McGraw-Hill, ISBN 0-256-13361-1.
9 Duranona, V., Sterling, M. and Baker, Ch. (2007), "An analysis of extreme non-synoptic winds", J. Wind Eng. Ind. Aerod., 95(9-11), 1007-1027.   DOI   ScienceOn
10 Elizondo, J., Martinez, J. and Probst, O. (2009), "Experimental study of a small wind turbine for low and medium wind regimes", Int. J. Energ. Res., 33(3), 309-326.   DOI   ScienceOn
11 Elizondo, J., Delgado. A., Martinez, J. and Probst, O. (2010), "Sistema de plegado para aerogenerador de 1.5 kW", Proceedings of the XXXIV Solar Energy Week, National Solar Energy Association (Mexico), Guanajuato, Mexico, October.
12 International Electrotechnical Commission. IEC61400-12 (1998): Wind turbine generator systems - Part 12: Wind turbine power performance testing, 1st Edition, Golden: National Wind Technology Center.
13 Kaimal, J.C., Wyngaard, J.C., Izumi Y. and Cote, O.R. (1972), "Spectral characteristics of surface-layer turbulence", Q. J. Roy. Meteor. Soc., 98, 563-589.   DOI
14 Monbet, V., Ailliot, P. and Prevosto, M. (2007), "Survey of stochastic models for wind and sea state time series", Probab. Eng. Mech., 22(2), 113-126.   DOI   ScienceOn
15 Kaimal, J.C. and Finnigan, J.F. (1994), Atmospheric boundary layer flows: their structure and measurement, Oxford University Press US.
16 Kareem, A. (2008), "Numerical simulation of wind effects: A probabilistic perspective", J. Wind Eng. Ind. Aerod., 96(10-11), 1472-1497.   DOI   ScienceOn
17 Mann, J., Ott, S., Hoffmann Jorgensen, B. and Frank, H.P. (2000), "WasP engineering 2000", Riso-R-1356 (EN)
18 Morales, A. and Probst, O. (2006), "The field performance of a small wind system", Proceedings of the WindPower 2006, American Wind Energy Association, Pittsburg, June.
19 Nfaoui, H., Essiarab, H. and Sayigh, A.A.M. (2004), "A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco", Renew. Energ., 29(8), 1407-1418.   DOI   ScienceOn
20 Priestley, M.B. (1981), Spectral analysis and time series, Probability and Mathematical Statistics, Academic Press. London.
21 Sahina, A.D. and Senb, Z. (2001), "First-order Markov chain approach to wind speed modelling", J. Wind Eng. Ind. Aerod., 89(3-4), 263-269.   DOI   ScienceOn
22 Saucier, R. (2000), "Computer generation of statistical distributions", Army Research Laboratory.
23 Seong, S.H. and Peterka, J.A. (1998), "Digital generation of surface-pressure fluctuations with spiky features", J. Wind Eng. Ind. Aerod., 73(2), 181-192.   DOI   ScienceOn
24 Shinozuka, M. (1971), "Simulation of multivariate and multidimensional random processes", J. Acoust. Soc. Am., 49(18), 357-368.   DOI
25 Sfetsos, A. (2000), "A comparison of various forecasting techniques applied to mean hourly wind speed time series", Renew. Energ., 21(1), 23-35.   DOI   ScienceOn
26 Verkaik, J.W. (2000), "Evaluation of two gustiness models for exposure correction calculations", J. Appl. Meteorol. Clim., 39(9), 1613-1626.   DOI   ScienceOn