skip to main content

Estimating Weibull Parameters for Wind Energy Applications using Seven Numerical Methods: Case studies of three costal sites in West Africa

1Laboratoire de Recherche en Sciences de l’Ingénieur (LARSI), Department of Electrical Engineering, Ecole Nationale Supérieure d’Ingénieurs (ENSI), University of Lomé, BP 1515, Lomé, Togo

2Laboratoire sur l'Energie Solaire (LES), Faculté Des Sciences (FDS) ), University of Lomé, BP 1515, Lomé, Togo

Received: 21 Apr 2019; Revised: 1 Jan 2020; Accepted: 19 Feb 2020; Available online: 4 May 2020; Published: 15 Jul 2020.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2020 The Authors. Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

In this study, the effectiveness of seven numerical methods is evaluated to determine the shape (K) and scale (C) parameters of Weibull distribution function for the purpose of calculating the wind speed characteristics and wind power density. The selected methods are graphical method (GPM), empirical method of Justus (EMJ), empirical method of Lysen (EML), energy pattern factor method (EPFM), maximum likelihood method (MLM) moment method (MOM) and the proposed. Hybrid method (HM) derived from EPFM and EMJ. The purpose is to identify the most appropriate method for computing the mean wind speed, wind speed standard deviation and wind power density for different costal locations in West Africa. Three costal sites (Lomé, Accra and Cotonou) are selected. The input data was collected, from January 2004 to December 2015 for Lomé site, from January 2009 to December 2015 for Accra site and from January 2009 to December 2012 for Cotonou. The results indicate that the precision of the computed mean wind speed, wind speed standard deviation and wind power density values change when different parameters estimation methods are used. Five of them which are EMJ, EML, EPF, MOM, ML, and HM method present very good accuracy while GPM shows weak ability for all three sites. 

Fulltext View|Download
Keywords: Modeling; Histogram of wind speed distribution; Weibull parameters estimation methods; Comparative evaluation

Article Metrics:

  1. Ajavon, A. S. A., Salami, A. A., Kodjo, M. K., & Bédja, K.-S. (2015). Comparative characterization study of the variability of wind energy potential by wind direction sectors for three coastal sites in Lomé, Accra and Cotonou. Journal of Power Technologies, 95(2), 134-142
  2. Akdağ, S. A., & Dinler, A. (2009). A new method to estimate Weibull parameters for wind energy applications. Energy Conversion and Management, 50(7), 1761-1766. https://doi.org/10.1016/j.enconman.2009.03.020
  3. Arslan, T., Bulut, Y. M., & Yavuz, A. A. (2014). Comparative study of numerical methods for determining Weibull parameters for wind energy potential. Renewable and Sustainable Energy Reviews, 40, 820-825. https://doi.org/10.1016/j.rser.2014.08.009
  4. Ayenagbo, K., Kimatu, J. N., & Rongcheng, W. (2011). A model for a sustainable energy supply strategy for the social-economic development of Togo. Journal of Economics and International Finance, 3(6), 387-398
  5. Azad, A., Rasul, M., & Yusaf, T. (2014). Statistical diagnosis of the best weibull methods for wind power assessment for agricultural applications. Energies, 7(5), 3056-3085. https://doi.org/10.3390/en7053056
  6. Brew-Hammond, A., & Kemausuor, F. (2009). Energy for all in Africa?to be or not to be?! Current Opinion in Environmental Sustainability, 1(1), 83-88. https://doi.org/10.1016/j.cosust.2009.07.014
  7. Celik, A. N. (2003). Energy output estimation for small-scale wind power generators using Weibull-representative wind data. Journal of Wind Engineering and Industrial Aerodynamics, 91(5), 693-707. https://doi.org/10.1016/S0167-6105(02)00471-3
  8. Chang, T. P. (2011). Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Applied Energy, 88(1), 272-282. https://doi.org/10.1016/j.apenergy.2010.06.018
  9. Deichmann, U., Meisner, C., Murray, S., & Wheeler, D. (2011). The economics of renewable energy expansion in rural Sub-Saharan Africa. Energy Policy, 39(1), 215-227. https://doi.org/10.1016/j.enpol.2010.09.034
  10. Dorvlo, A. S. S. (2002). Estimating wind speed distribution. Energy Conversion and Management, 43(17), 2311-2318. https://doi.org/10.1016/S0196-8904(01)00182-0
  11. Genc, A., Erisoglu, M., Pekgor, A., Oturanc, G., Hepbasli, A., & Ulgen, K. (2005). Estimation of wind power potential using Weibull distribution. Energy Sources, 27(9), 809-822. https://doi.org/10.1080/00908310490450647
  12. Jamieson, P. D., Porter, J. R., & Wilson, D. R. (1991). A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Research, 27(4), 337-350. https://doi.org/10.1016/0378-4290(91)90040-3
  13. Jowder, F. A. L. (2009). Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain. Applied Energy, 86(4), 538-545. https://doi.org/10.1016/j.apenergy.2008.08.006
  14. Justus, C. G., Hargraves, W. R., Mikhail, A., & Graber, D. (1978). Methods for estimating wind speed frequency distributions. Journal of Applied Meteorology, 17(3), 350-353. https://doi.org/10.1175/1520-0450(1978)017<0350:MFEWSF>2.0.CO;2
  15. Keyhani, A., Ghasemi-Varnamkhasti, M., Khanali, M., & Abbaszadeh, R. (2010). An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran. Energy, 35(1), 188-201. https://doi.org/10.1016/j.energy.2009.09.009
  16. Leung, D. Y. C., & Yang, Y. (2012). Wind energy development and its environmental impact: A review. Renewable and Sustainable Energy Reviews, 16(1), 1031-1039. https://doi.org/10.1016/j.rser.2011.09.024
  17. Li, M.-F., Tang, X.-P., Wu, W., & Liu, H.-B. (2013). General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Conversion and Management, 70, 139-148. https://doi.org/10.1016/j.enconman.2013.03.004
  18. Lu, L., Yang, H., & Burnett, J. (2002). Investigation on wind power potential on Hong Kong islands?an analysis of wind power and wind turbine characteristics. Renewable Energy, 27(1), 1-12. https://doi.org/10.1016/S0960-1481(01)00164-1
  19. Mentis, D., Hermann, S., Howells, M., Welsch, M., & Siyal, S. H. (2015). Assessing the technical wind energy potential in Africa a GIS-based approach. Renewable Energy, 83, 110-125. https://doi.org/10.1016/j.renene.2015.03.072
  20. Mohammadi, K., Alavi, O., Mostafaeipour, A., Goudarzi, N., & Jalilvand, M. (2016). Assessing different parameters estimation methods of Weibull distribution to compute wind power density. Energy Conversion and Management, 108, 322-335. https://doi.org/10.1016/j.enconman.2015.11.015
  21. Mostafaeipour, A., Sedaghat, A., Dehghan-Niri, A. A., & Kalantar, V. (2011). Wind energy feasibility study for city of Shahrbabak in Iran. Renewable and Sustainable Energy Reviews, 15(6), 2545-2556. https://doi.org/10.1016/j.rser.2011.02.030
  22. Rocha, P. A. C., de Sousa, R. C., de Andrade, C. F., & 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, 89(1), 395-400. https://doi.org/10.1016/j.apenergy.2011.08.003
  23. Safari, B. (2011). Modeling wind speed and wind power distributions in Rwanda. Renewable and Sustainable Energy Reviews, 15(2), 925-935. https://doi.org/10.1016/j.rser.2010.11.001
  24. Salami, Adekunlé Akim, Ajavon, A. S. A., Kodjo, M. K., Ouedraogo, S., & Bédja, K.-S. (2018). The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. International Journal of Renewable Energy Development, 7(2), 139-150. https://doi.org/10.14710/ijred.7.2.139-150
  25. Salami, Akim A, Ajavon, A. S. A., Kodjo, M. K., & Bedja, K.-S. (2016). Evaluation of Wind Potential for an Optimum Choice of Wind Turbine Generator on the Sites of Lomé, Accra, and Cotonou Located in the Gulf of Guinea. International Journal of Renewable Energy Development, 5(3). https://doi.org/10.14710/ijred.5.3.211-223
  26. Salami, Akim Adekunle, 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. https://doi.org/10.1016/j.renene.2012.06.057
  27. 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(1), 75-84. https://doi.org/10.1016/S0167-6105(99)00122-1
  28. Stevens, M. J. M., & Smulders, P. T. (1979). The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes. Wind Engineering, 132-145

Last update:

  1. Assessing the performance of several numerical methods for estimating Weibull parameters for Wind Energy Applications: A case study of Al-Hodeidah in Yemen

    Waleed S.A. Hasan, Ali Saif M. Hassan, Mohamed A. Shukri. Energy Reports, 10 , 2023. doi: 10.1016/j.egyr.2023.09.081
  2. Estimating mixture hybrid Weibull distribution parameters for wind energy application using Bayesian approach

    Agbassou Guenoupkati, Adekunlé Akim Salami, Yao Bokovi, Piléki Xavier Koussetou, Seydou Ouedraogo. International Journal of Renewable Energy Development, 12 (5), 2023. doi: 10.14710/ijred.2023.54452
  3. Modelling the Optimal Electricity Mix for Togo by 2050 Using OSeMOSYS

    Esso-Wazam Honoré Tchandao, Akim Adekunlé Salami, Koffi Mawugno Kodjo, Amy Nabiliou, Seydou Ouedraogo. International Journal of Renewable Energy Development, 12 (2), 2023. doi: 10.14710/ijred.2023.50104
  4. Wind Energy Resource Assessment for Cook Islands With Accurate Estimation of Weibull Parameters Using Frequentist and Bayesian Methods

    Krishneel A. Singh, M. G. M. Khan, Mohammed Rafiuddin Ahmed. IEEE Access, 10 , 2022. doi: 10.1109/ACCESS.2022.3156933
  5. Simulation of Wind Speeds with Spatio-Temporal Correlation

    Moisés Cordeiro-Costas, Daniel Villanueva, Andrés E. Feijóo-Lorenzo, Javier Martínez-Torres. Applied Sciences, 11 (8), 2021. doi: 10.3390/app11083355
  6. Assessing the wind energy potential in provinces of West Java, Papua, and East Borneo in Indonesia

    Thariq Wijanarko, Djamal Didane, Wijianto Wijianto, Mohanad Al-Ghriybah, Nurul Nasir, Isa Mat. Journal of Applied Engineering Science, 20 (4), 2022. doi: 10.5937/jaes0-35192
  7. Wind power density characterization in arid and semi-arid Taita-Taveta and Garissa counties of Kenya

    Ibrahim Kipngeno Rotich, Peter K. Musyimi. Cleaner Engineering and Technology, 17 , 2023. doi: 10.1016/j.clet.2023.100704
  8. Approaches in performance and structural analysis of wind turbines – A review

    Sakthivel Rajamohan, Abhiram Vinod, Mantri Pragada Venkata Sesha Aditya, Harshini Gopalakrishnan Vadivudaiyanayaki, Van Nhanh Nguyen, Müslüm Arıcı, Sandro Nižetić, Thi Thai Le, Rahmat Hidayat, Dinh Tuyen Nguyen. Sustainable Energy Technologies and Assessments, 53 , 2022. doi: 10.1016/j.seta.2022.102570
  9. Evaluation of the performance of Five Distribution Functions for Estimating Weibull Parameters for wind energy potential in Nigeria

    E.F. Nymphas, R.O. Teliat. Scientific African, 2023. doi: 10.1016/j.sciaf.2023.e02037
  10. The Potential of Wind Energy and Design Implications on Wind Farms in Saudi Arabia

    Muhammad Tayyab Naqash, Mohammad Hasan Aburamadan, Ouahid Harireche, Abdulrahman AlKassem, Qazi Umar Farooq. International Journal of Renewable Energy Development, 10 (4), 2021. doi: 10.14710/ijred.2021.38238

Last update: 2024-06-21 01:11:30

No citation recorded.