Influence of the Determination Methods of K and C Parameters on the Ability of Weibull Distribution to Suitably Estimate Wind Potential and Electric Energy

*Ruben M. Mouangue -  Department of Energetic Engineering, UIT, UN, PO Box 455 Ngaoundere, Cameroon, Cameroon
Myrin Y. Kazet -  Department of GEEA, PAI, ENSAI, University of Ngaoundere, Cameroon
Alexis Kuitche -  Department of GEEA, PAI, ENSAI, University of Ngaoundere, Cameroon
Jean-Marie Ndjaka -  Department of Physics, Faculty of Sciences, University of Yaounde I, Cameroon
Published: 15 Jul 2014.
Open Access
Citation Format:
Article Info
Section: Original Research Article
Language: EN
Full Text:
In Vol 3, No 2 (2014): July 2014
Statistics: 595 929
Abstract

The modeling of the wind speed distribution is of great importance for the assessment of wind energy potential and the performance of wind energy conversion system. In this paper, the choice of two determination methods of Weibull parameters shows theirs influences on the Weibull distribution performances. Because of important calm winds on the site of Ngaoundere airport, we characterize the wind potential using the approach of Weibull distribution with parameters which are determined by the modified maximum likelihood method. This approach is compared to the Weibull distribution with parameters which are determined by the maximum likelihood method and the hybrid distribution which is recommended for wind potential assessment of sites having nonzero probability of calm. Using data provided by the ASECNA Weather Service (Agency for the Safety of Air Navigation in Africa and Madagascar), we evaluate the goodness of fit of the various fitted distributions to the wind speed data using the Q – Q plots, the Pearson’s coefficient of correlation, the mean wind speed, the mean square error, the energy density and its relative error. It appears from the results that the accuracy of the Weibull distribution with parameters which are determined by the modified maximum likelihood method is higher than others. Then, this approach is used to estimate the monthly and annual energy productions of the site of the Ngaoundere airport. The most energy contribution is made in March with 255.7 MWh. It also appears from the results that a wind turbine generator installed on this particular site could not work for at least a half of the time because of higher frequency of calm. For this kind of sites, the modified maximum likelihood method proposed by Seguro and Lambert in 2000 is one of the best methods which can be used to determinate the Weibull parameters.

Keywords
frequency of calm; Weibull parameters; wind energy; wind modeling; wind potential

Article Metrics:

  1. ASECNA (2012) Archives data of the weather station of Ngaoundere.
  2. Bataineh, K.M. & Dalalah, D. (2013) Assessment of wind energy po- tential for selected areas in Jordan. Renewable Energy, 59, 75-81.
  3. Boudia, S.M., Benmansour, A., Ghellai, N., Benmedjahed, M. & Hellal, M.A.T. (2013). Temporal assessment of wind energy resource at four locations in Algerian Sahara. Energy Conversion and Management, 76, 654–664.
  4. Carta, J.A., Ramírez, P. & Velázquez, S. (2008) Influence of the level of fit of a density probability function to wind-speed data on the WECS mean power output estimation. Energy Conversion and Management, 49, 2647–2655.
  5. Đurišić, Ž. & Mikulović, J. (2012) A model for vertical wind speed data extrapolation for improving wind resource assessment using WAsP. Renewable Energy, 41, 407–411.
  6. Gibbons, J., Chakraborti, S. (2003) Nonparametric statistical inference. CRC Press.
  7. Jamil, M., Parsa, S. & Majidi, M. (1995) Wind power statistics and an evaluation of wind energy density. Renewable Energy, 6, No. 5-6, 623–628.
  8. Kaldellis, J.K. (2008) Maximum wind potential exploitation in autonomous electrical networks on the basis of stochastic analysis. Journal of Wind Engineering and Industrial Aerodynamics, 96, 1412– 1424.
  9. Kazet, M., Mouangue, R., Kuitche, A., Ndjaka, J.M. & Takam, S. (2013) Modélisation et simulation numérique des données du vent en vue d’une prédiction de l’énergie électrique d’origine éolienne : cas d’un site de la ville de Ngaoundere au Cameroun. Revue des Energies Renouvelables, 16(3), 527 – 538.
  10. Lackner, M.A., Rogers, A.L., Manwell, J.F. & McGowan, J.G. (2010) A new method for improved hub height mean wind speed estimates using short-term hub height data. Renewable Energy, 35, 2340–2347.
  11. Li, M. & Li, X. (2005) MEP-type distribution function: a better alternative to Weibull function for wind speed distributions. Renewable Energy, 30, 1221–1240.
  12. Morales, L., Lang, F. & Mattar, C. (2012) Mesoscale wind speed simulation using CALMET model and reanalysis information: An application to wind potential. Renewable Energy, 48, 57–71.
  13. Omer, A.M. (2008) On the wind energy resources of Sudan. Renewable and Sustainable Energy Reviews, 12, 2117–2139.
  14. Rahman, M.M., Mostafiz, S.B., Paatero, J.V. & Lahdelma, R. (2014) Extension of energy crops on surplus agricultural lands: A potentially viable option in developing countries while fossil fuel reserves are diminishing. Renewable and Sustainable Energy Reviews, 29, 108–119.
  15. Ramírez, P. & Carta, J.A. (2005) Influence of the data sampling interval in the estimation of the parameters of the Weibull wind speed probability density distribution: a case study. Energy Conversion and Management, 46, 2419–2438.
  16. Safari, B. & Gasore, J. (2010) A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda. Renewable Energy, 35, 2874–2880.
  17. Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. & Bedja, K.S. (2013) Contribution to improving the modeling of wind and evaluation of the wind potential of the site of Lome: Problems of taking into account the frequency of calm winds. Renewable Energy, 50, 449–455.
  18. Sathyajith, M. (2006) Wind Energy Fundamentals, Resource Analysis and Economics, pp. 68-83. Springer-Verlag Berlin Heidelberg.
  19. Sathyajith, M. & Geeta, S.P. (2011) Advances in Wind Energy Conversion Technology, pp. 74-80. Springer-Verlag Berlin Heidelberg.
  20. Seguro, J.V. & Lambert, T.W. (2000) Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. Journal of Wind Engineering and Industrial Aerodynamics, 85, 75–84.
  21. Takle, E.S. & Brown, J.M. (1978) Note on the Use of Weibull Statistics to Characterize Wind–Speed Data. Journal of Applied Meteorology, 17, 556–559.
  22. Troen, I. & Petersen, E.L. (1989) European wind atlas. 1st ed. Denmark: Risø National Laboratory.
  23. Vestas technology documentation (2005) General specification of V82-1.65 MW. http://www.vestas.com/Files/Filer/EN/Brochures/Pro ductBrochureV821_65_UK.pdf. Accessed on 12 December 2013.
  24. Zhang, J., Chowdhurya, S., Messac, A. & Castillo, L. (2013) A Multivariate and Multimodal Wind Distribution model. Renewable Energy, 51, 436–447.

The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to International Journal of Renewable Energy Development and Center of Biomass and Renewable Energy, Department of Chemical Engineering Diponegoro University as publisher of the journal.

Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , will be allowed only with a written permission from International Journal of Renewable Energy Development and Center of Biomass and Renewable Energy, Department of Chemical Engineering Diponegoro University.

International Journal of Renewable Energy Development and Center of Biomass and Renewable Energy, Department of Chemical Engineering Diponegoro University, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in the International Journal of Renewable Energy Development are sole and exclusive responsibility of their respective authors and advertisers.