Volume 5, Issue 1, June 2019, Page: 8-14
A Wind Climate Dynamic Modeling and Control Using Weibull and Extreme Value Distribution System
Tim Chen, Laboratoire d’Energies Renouvelables, École Supérieure Polytechnique de Dakar, Dakar, Senegal
Alfred Hausladen, NAAM Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
Jonathan Sstamler, Department of Physiological Sciences, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
Dneil Granger, Nuclear Power Corporation of India Limited, Mumbai, India
Abu Hurayraasiv Khanand, Department of Electrical and Computer Engineering, Northsouth University, Taka, Bangladesh
Johncy Cheng, Department of Electronic and Automatic Engineering, Covenant University, Ota Ogun State, Nigeria
Cwc Chen, Parallel CFD and Optimization Unit, Lab. of Thermal Turbomachines, School of Mechanical Engineering, National Technical University of Athens, Athens, Greece; Department of Electrical and Computer Engineering, Asia University, Mssbaai, Taiwan, China
Chariklia Ageorgopoulou Kyriakos, Department of Electrical and Computer Engineering, Asia University, Mssbaai, Taiwan, China
Received: Dec. 4, 2018;       Accepted: Feb. 26, 2019;       Published: Apr. 8, 2019
DOI: 10.11648/j.fm.20190501.12      View  92      Downloads  12
Abstract
The dynamics of wind velocity data modeling plays a crucial role for the estimation of wind load and wind energy. Apart from these, the same modeling must also be used in the load cycle analysis of fatigue failure in slender structures to address periodic vortex shedding. Most authors fitted wind velocities of various locations using Weibull model. However, they did not check the validity of the model in describing the range of extreme wind velocity, which is not clear from the usual graphical representation. In this work, the validity of Weibull model for describing parent as well as extreme hourly mean wind velocity data for four places on the east coast of India has been checked; Weibull model has been found to become inappropriate for describing wind velocity in the range of extremes.
Keywords
Weibull Distribution, Wind Velocities, Non-Exceedance Probability, Gumbel Distribution, Chauvenet’s Criterion, Probability Factor
To cite this article
Tim Chen, Alfred Hausladen, Jonathan Sstamler, Dneil Granger, Abu Hurayraasiv Khanand, Johncy Cheng, Cwc Chen, Chariklia Ageorgopoulou Kyriakos, A Wind Climate Dynamic Modeling and Control Using Weibull and Extreme Value Distribution System, Fluid Mechanics. Vol. 5, No. 1, 2019, pp. 8-14. doi: 10.11648/j.fm.20190501.12
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Akdağ, S. A., and Güler, Ö. (2010). “Evaluation of wind energy investment interest and electricity generation cost analysis for Turkey.” Applied Energy, Vol. 87, Issue 8, pp. 2574–2580, DOI: 10.1016/J.APENERGY.2010.03.015.
[2]
Beccali, M., Cirrincione, G., Marvuglia, A., and Serporta, C. (2010). “Estimation of wind velocity over a complex terrain using the generalized mapping regressor.’’ Applied Energy, Vol. 87, Issue 3, pp. 884–893, DOI: 10.1016/J.APENERGY.2009.05.026.
[3]
Bivona, S., Burlon, R., and Leone, C. (2003). ‘‘Hourly wind velocity analysis in Sicily.’’ Renewable Energy, Vol. 28, Issue 9, pp. 1371-1385, DOI: 10.1016/S0960-1481(02)00230-6.
[4]
Calif, R. (2012). “PDF models and synthetic model for the wind velocity fluctuations based on the resolution of Langevin equation.” Applied Energy, Vol. 99, pp. 173–182, DOI: 10.1016/J.APENERGY.2012.05.007.
[5]
Carapellucci, R., and Giordano, L. (2013). “A methodology for the synthetic generation of hourly wind velocity time series based on some known aggregate input data.” Applied Energy, Vol. 101, pp. 541–550, DOI: 10.1016/J.APENERGY.2012.06.044.
[6]
Carvalho, D., Rocha, A., Santos, C. S., and Pereira, R. (2013). “Wind resource modelling in complex terrain using different mesoscale–microscale coupling techniques.” Applied Energy, Vol. 108, pp. 493–504, DOI:10.1016/J.APENERGY.2013.03.074.
[7]
Castillo, E., Hadi, A. S., Balakrishnan, N., and Sarabia, J. M. (2005). Extreme value and related models with applications in engineering and science, Wiely-Interscience, John Wiely & Sons, Inc., New Jersey.
[8]
Celik, A. N. (2004). “On the distributional parameters used in assessment of the suitability of wind velocity probability density functions.” Energy Conversion and Management, Vol. 45, Issues 11-12, pp. 1735-1747, DOI:10.1016/j.enconman.2003.09.027.
[9]
Celik, A.N., and Kolhe, M. (2013). “Generalized feed-forward based method for wind energy prediction.” Applied Energy, Vol. 101, pp. 582–588, DOI: 10.1016/J.APENERGY.2012.06.040.
[10]
Chang, T. P. (2011). “Estimation of wind energy potential using different probability density functions.” Applied Energy, Vol. 88, Issue 5, pp. 1848–1856, DOI: 10.1016/J.APENERGY.2010.11.010.
[11]
Chen, K. and Yu, J. (2014). “Short-term wind velocity prediction using an unscented Kalman filter based state- space support vector regression approach.” Applied Energy, Vol. 113, pp. 690–705, DOI: 10.1016/J.APENERGY.2013.08.025.
[12]
Cook, N. J. (2001). “Discussion on ‘modern estimation of the parameters of the Weibull wind velocity distribution for wind energy analysis’ by J. V. Seguro, T. W. Lambert.” Journal of Wind Engineering and Industrial Aerodynamics, Vol. 89, Issue 10, pp. 867-869, DOI: 10.1016/S0167-6105(00)00088-X.
[13]
Deaves, D. M., and Lines, I. G. (1997). “On the fitting of low mean wind velocity data to the Weibull distribution.” Journal of Wind Engineering and Industrial Aerodynamics, Vol. 66, Issue 3, pp. 169-178, DOI: 10.1016/S0167- 6105(97)00013-5.
[14]
Douak, F., Melgani, F., and Benoudjit, N. (2013). “Kernel ridge regression with active learning for wind velocity prediction.” Applied Energy, Vol. 103, pp. 328–340, DOI: 10.1016/J.APENERGY.2012.09.055.
[15]
EI-Wakil, M. M. (2002). Power plant technology, Mc-Graw-hill International Editions, Electrical and Mechanical Engineering Series.
[16]
Fadare, D. A. (2010). “The application of artificial neural networks to mapping of wind velocity profile for energy application in Nigeria.” Applied Energy, Vol. 87, Issue 3, pp. 934–942, DOI: 10.1016/J.APENERGY.2009.09.005.
[17]
Garcia A., Torres J. L., Prieto E., and Francisco, A. de (1998). “Fitting wind velocity distributions: a case study.” Solar Energy, Vol. 62, No. 2, pp. 139-144, DOI: 10.1016/S0038-092X(97)00116-3.
[18]
Gumbel, E. J. (1958). Statistics of extremes, Columbia Univ. press, New York.
[19]
Gupta, B. K. (1986). “Weibull parameters for annual and monthly wind velocity distributions for five locations in India.” Solar Energy, Vol. 37, No. 6, pp. 469-471, DOI:10.1016/0038-092X(86)90039-3.
[20]
Harris, R. I., and Cook, N. J. (2014). “The parent wind velocity distribution: why Weibull?” Journal of Wind Engineering and Industrial Aerodynamics, Vol. 131, pp. 72-87, DOI: 10.1016/J.JWEIA.2014.05.005.
[21]
Jung, S., ArdaVanli, O., and Kwon, S. (2013). “Wind energy potential assessment considering the uncertainties due to limited data.” Applied Energy, Vol. 102, pp. 1492–1503, DOI: 10.1016/J.APENERGY.2012.09.011.
[22]
IS: 875 (Part III)-2015, Design Loads (Other than Earthquake) for Buildings and Structures – Code of Practice, New Delhi.
[23]
Kasperski, M. (2009). “Specification of the design wind load- A critical review of code concepts.” Journal of Wind Engineering and Industrial Aerodynamics, Vol. 97, Issues 7-8, pp. 335-357, DOI: 10.1016/j.jweia.2009.05.002.
[24]
Kasperski, M. (2010). “Estimation of design wind velocity.” Proceedings of 7th International Advanced School on Wind Engineering, New Delhi, India.
[25]
Lujano-Rojas, J. M., Dufo-López, R., and Bernal-Agustín, J. L. (2012). “Optimal sizing of small wind/battery systems considering the DC bus voltage stability effect on energy capture, wind velocity variability, and load uncertainty.” Applied Energy, Vol. 93, pp. 404–412, DOI: 10.1016/J.APENERGY.2011.12.035.
[26]
Lun, I. Y. F., and Lam, J. C. (2000). “A study of Weibull parameters using long term wind observations.” Renewable Energy, Vol. 20, Issue 2, pp. 145-153, DOI: 10.1016/S0960-1481(99)00103-2.
[27]
Morales, J. M., Mínguez, R., and Conejo, A. J. (2010). “A methodology to generate statistically dependent wind velocity scenarios.” Applied Energy, Vol. 87, Issue 3, pp. 843–855, DOI: 10.1016/J.APENERGY.2009.09.022.
[28]
Rehman, S., Halawani, T. O., and Husain, T. (1994). “Weibull parameters for wind velocity distribution in Saudi Arabia.” Solar Energy, Vol. 53, No. 6, pp. 473-479, DOI: 10.1016/0038-092X(94)90126-M.
[29]
Rocha, P. A. C., de Sousa, R. C., de Andrade, C. F., and da Silva, M. E. V. (2012). “Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil.” Applied Energy, Vol. 89, Issue 1, pp. 395-400, DOI: 10.1016/J.APENERGY.2011.08.003.
[30]
Sarkar, A., Gugliani, G., and Deep, S. (2017). “Weibull model for wind velocity data analysis of different locations in India.” KSCE Journal of Civil Engineering, Vol. 21, Issue 7, pp. 2764-2776, DOI: 10.1007/s12205-017-0538- 5.
[31]
Sarkar, A., Kumar, N., and Mitra, D. (2014). “Extreme wind climate modeling of some locations in India for the specification of the design wind velocity of structures.” KSCE Journal of Civil Engineering, Vol. 18, Issue 5, pp. 1496-1504, DOI: 10.1007/s12205-014-0428-z.
[32]
Seguro, J. V., and Lambert, T. W. (2000). “Modern estimation of the parameters of the Weibull wind velocity distribution for wind energy analysis.” Journal of Wind Engineering and Industrial Aerodynamics, Vol. 85, Issue 1, pp. 75-84, DOI: 10.1016/S0167-6105(99)00122-1.
[33]
Soukissian, T. (2013). “Use of multi-parameter distributions for offshore wind velocity modeling: the Johnson S B distribution.” Applied Energy, Vol. 111, pp. 982–1000, DOI: 10.1016/J.APENERGY.2013.06.050.
[34]
Sulaiman, M. Y., Akaak, A. M., Wahab, M. A., Zakaria, A., Sulaiman, Z. A., and Suradi, J. (2002). “Wind characteristic of Oman.” Energy, Vol. 27, Issue 1, pp. 35-46, DOI: 10.1016/S0360-5442(01)00055-X.
[35]
Thiaw, L., Sow, G., Fall, S. S., Kasse, M., Sylla E., and Thioye, S. (2010). “A neural network based approach for wind resource and wind generators production assessment.” Applied Energy, Vol. 87, Issue 5, pp. 1744–1748, DOI: 10.1016/J.APENERGY.2009.10.001.
[36]
Usta, I., and Kantar, Y. M. (2012). “Analysis of some flexible families of distributions for estimation of wind velocity distributions.” Applied Energy, Vol. 89, Issue 1, pp. 355–367, DOI:10.1016/J.APENERGY.2011.07.045.
[37]
Zaharim, A., Razali, A. M., Abidin, R. Z., and Sopian, K. (2009). “Fitting of statistical distributions to wind velocity data in Malaysia.” European Journal of Scientific Research, Vol. 26, No. 1, pp. 6-12.
[38]
Zárate-Miñano, R., Anghel, M., and Milano, F. (2013). “Continuous wind velocity models based on stochastic differential equations.” Applied Energy, Vol. 104, pp. 42–49, DOI: 10.1016/J.APENERGY.2012.10.064.
[39]
Zhang, H., Yu, Y., and Liu, Z. (2014). “Study on the maximum entropy principle applied to the annual wind velocity probability distribution: A case study for observations of inter tidal zone anemometer towers of Rudong in East China sea.” Applied Energy, Vol. 114, pp. 931–938, DOI: 10.1016/J.APENERGY.2013.07.040.
Browse journals by subject