DOI: https://doi.org/10.14710/ijred.3.2.145-154

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

1Department of Energetic Engineering, UIT, UN, PO Box 455 Ngaoundere, Cameroon, Cameroon

2Department of GEEA, PAI, ENSAI, University of Ngaoundere, Cameroon

3Department of Physics, Faculty of Sciences, University of Yaounde I, Cameroon

Published: 15 Jul 2014.
Editor(s):

Citation Format:
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.

Fulltext View|Download
Keywords: frequency of calm; Weibull parameters; wind energy; wind modeling; wind potential

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Economic feasibility of large scale PV water pumping applications utilizing real field data for a case study in Jordan Tin (II) Chloride Catalyzed Esterification of High FFA Jatropha Oil: Experimental and Kinetics Study Castor Seed from Melkasa Agricultural Research Centre, East Showa, Ethiopia and it’s biodiesel performance in Four Stroke Diesel Engine More recent articles
Most cited articles
Numerical Study of Effect of Blade Twist Modifications on the Aerodynamic Performance of Wind Turbine CFD Investigation of A New Elliptical-Bladed Multistage Savonius Rotors Integration of 5G Technologies in Smart Grid Communication-A Short Survey Power Quality Improvement Wind Energy System Using Cascaded Multilevel Inverter Analysing the Possibility of Extracting Energy from Ocean Waves in Cabo-Verde to Produce Clean Electricity - Case-Study: the Leeward Islands More cited articles
  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

Last update:

  1. Wind energy of Cameroon by determining Weibull parameters: potential of a environmentally friendly energy

    R. H. Tonsie Djiela, P. Tiam Kapen, G. Tchuen. International Journal of Environmental Science and Technology, 18 (8), 2021. doi: 10.1007/s13762-020-02962-z

  2. Statistical analysis and elaboration of the wind potential map of the city of Bangui (Central African Republic)

    Judith F. Ngbara Touafio, Salomon Malenguiza, Sanda Oumarou, Myrin Y. Kazet, Ruben M. Mouangue. Renewable Energy Focus, 29 , 2019. doi: 10.1016/j.ref.2019.01.001

  3. Evaluation of projected decadal wind energy potential in Chiang Mai

    Pakpoom Ratjiranukool, Sujittra Ratjiranukool. Journal of Physics: Conference Series, 2145 (1), 2021. doi: 10.1088/1742-6596/2145/1/012048

  4. Analysis of a wind turbine project in the city of Bouar (Central African Republic)

    Judith F. Ngbara Touafio, Oumarou Sanda, Salomon Malenguinza, Jean M’Boliguipa, Ruben M. Mouangue. Scientific African, 8 , 2020. doi: 10.1016/j.sciaf.2020.e00354

  5. Wind Energy Resource Assessment in Ngaoundere Locality

    Myrin Y. Kazet, Ruben Mouangue, Alexis Kuitche, J.M. Ndjaka. Energy Procedia, 93 , 2016. doi: 10.1016/j.egypro.2016.07.152

  6. Technoeconomic Analysis of a Wind Power Generation System and Wind Resource Mapping Using GIS Tools: The Case of Twelve Locations in the Commune of Evodoula, Cameroon

    Vincent De Paul Igor Essouma Koung, Francis Daniel Menga, Beguide Bonoma, Jean Luc Nsouandele, Ruben Martin Mouangue, Ayman Al-Quraan. International Journal of Energy Research, 2024 , 2024. doi: 10.1155/2024/8825472

  7. Nonlinear backstepping control for PMSG wind turbine used on the real wind profile of the Dakhla‐Morocco city

    Youness El Mourabit, Aziz Derouich, Abdelaziz El Ghzizal, Najib El Ouanjli, Othmane Zamzoum. International Transactions on Electrical Energy Systems, 30 (4), 2020. doi: 10.1002/2050-7038.12297

  8. Strain engineering for optimized hydrogen storage: Enhancing ionic conductivity and achieving near-room-temperature desorption in Mg₂NiH₄

    Amine Alaoui-Belghiti, Abdelmajid Assila, Ikram Belkoufa, Mourad Rkhis, Said Laasri, Mouhaydine Tlemçani, El-kebir Hlil, Abdelowahed Hajjaji. International Journal of Hydrogen Energy, 92 , 2024. doi: 10.1016/j.ijhydene.2024.10.292

  9. The Impact of Wind Power and Load Power Fluctuations on Energy Storage Sizing

    Mykhailo Kostiantynovych Yaremenko, Kateryna Serhiivna Klen. Microsystems, Electronics and Acoustics, 28 (3), 2023. doi: 10.20535/2523-4455.mea.290932

Last update: 2024-11-03 23:46:52

  1. Wind resource assessment in the city of N'Djamena in chad

    Abdraman M.. International Journal of Renewable Energy Research, 6 (3), 2016.

  2. Wind energy of Cameroon by determining Weibull parameters: potential of a environmentally friendly energy

    R. H. Tonsie Djiela, P. Tiam Kapen, G. Tchuen. International Journal of Environmental Science and Technology, 18 (8), 2021. doi: 10.1007/s13762-020-02962-z

  3. Statistical analysis and elaboration of the wind potential map of the city of Bangui (Central African Republic)

    Judith F. Ngbara Touafio, Salomon Malenguiza, Sanda Oumarou, Myrin Y. Kazet, Ruben M. Mouangue. Renewable Energy Focus, 29 , 2019. doi: 10.1016/j.ref.2019.01.001

  4. Analysis of a wind turbine project in the city of Bouar (Central African Republic)

    Judith F. Ngbara Touafio, Oumarou Sanda, Salomon Malenguinza, Jean M’Boliguipa, Ruben M. Mouangue. Scientific African, 8 , 2020. doi: 10.1016/j.sciaf.2020.e00354

  5. Nonlinear backstepping control for PMSG wind turbine used on the real wind profile of the Dakhla-Morocco city

    El Mourabit Y.. International Transactions on Electrical Energy Systems, 30 (4), 2020. doi: 10.1002/2050-7038.12297