10.1007/s40095-021-00402-3

Potential of wind energy in Cameroon based on Weibull, normal, and lognormal distribution

  • Christian Kenfack-Sadem*1,
  • Raphaël Tagne2,
  • François Beceau Pelap2,
  • Gerard Nfor Bawe2,
  1. Laboratory of Condensed Matter-Electronics and Signal Processing (LAMACET), Department of Physics, University of Dschang, Dschang, CM Laboratory for Environmental Modelling and Atmospheric Physics, Department of Physics, University of Yaounde 1, Yaounde, CM International Chair in Mathematical Physics and Applications, University of Abomey-Calavi, Cotonou, BJ
  2. Unité de Recherche de Mécanique Et de Modélisation Des Systèmes Physiques (UR-2MSP), Faculté des Sciences, Département de Physique, Université de Dschang, Dschang, CM

Published in Issue 2021-06-15

How to Cite

Kenfack-Sadem, C., Tagne, R., Pelap, F. B., & Nfor Bawe, G. (2021). Potential of wind energy in Cameroon based on Weibull, normal, and lognormal distribution. International Journal of Energy and Environmental Engineering, 12(4 (December 2021). https://doi.org/10.1007/s40095-021-00402-3
Crossref
Scopus
Google Scholar
Europe PMC

Abstract

Abstract Careful analysis of long-term wind data in a broad area is essential to estimate the wind energy potential of a region. For this purpose, knowledge of wind speed distribution is an essential task. This paper proposes a comprehensive statistical evaluation of monthly, annual, and interannual variabilities of mean wind speeds and wind power densities of 2745 different sites over an area covering the whole of Cameroon. We used wind speed data obtained from ERA-5 for the period 2000–2017. In addition to the popular Weibull probability density function (WPDF), other continuous distributions such as the Normal PDF (NPDF) and the Lognormal PDF (LPDF) were used to investigate their suitability for the description of the wind regimes. We also established the analytical expression of the Normal power density. Three statistical analysis tools were used to determine the goodness of fit of the curves and the parameters of the PDFs were determined by the maximum likelihood method. We found that the theoretical power (TP) and power densities based on WPDF and NPDF are closely related. We observed the highest wind speeds and power densities in a few areas in the Lake Chad Basin and the Gulf of Guinea (GoG) which did not exceed class 2. It is seen that most of the areas studied show relatively low wind speeds and power densities that belong mostly to class 1 during all seasons. These results are useful for sizing wind turbines specific for different locations in Cameroon and making good decisions in terms of budgetary optimization of investments in the energy sector.

Keywords

  • Wind speed data,
  • PDFs,
  • Power density (PD),
  • Goodness of fit test

References

  1. Erasmus et al. (2017) Sustainable energy policies in Cameroon: a holistic overview https://doi.org/10.1016/j.rser.2017.10.049
  2. Kasra et al. (2014) Electricity generation and energy cost estimation of large-scale wind turbines in Jarandagh, Iran https://doi.org/10.1155/2014/613681
  3. Tchinda et al. (2000) Estimation of mean wind energy available in far north Cameroon 41(17) (pp. 1917-1929) https://doi.org/10.1016/S0196-8904(00)00017-0
  4. Afungchui (2014) Rayleigh distribution-based model for prediction of wind energy potential of Cameroon 1(2) (pp. 26-43)
  5. Jourdier (2020) “Evaluation of ERA5, MERRA-2, COSMO-REA6, NEWA and AROME to simulate wind power production over France”, Copernicus Publications (pp. 63-77) https://doi.org/10.5194/asr-17-63-2020
  6. Jeutho et al. (2018) How to use the temperature data to find the appropriate site for best wind speed generation? Application on data obtained from three different cities of Cameroon 2(4) (pp. 53-62)
  7. Aynuar and Figen (2008) A seasonal analysis of wind turbine characteristics and wind power potential in Manisa, Turkey 5(6) (pp. 466-479) https://doi.org/10.1080/15435070802498101
  8. Afungchui and Aban (2014) Analysis of wind regimes for energy estimation in Bamenda, of the North West Region of Cameroon, based on the Weibull distribution 17(1) (pp. 137-147)
  9. Stevens and Smulders (1979) The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes 3(6) (pp. 32-45)
  10. Lun and Lam (2000) A study of Weibull parameters using long-term wind observations 20(2) (pp. 145-153) https://doi.org/10.1016/S0960-1481(99)001032
  11. Tchinda and Kaptouom (2003) Wind energy in Adamaoua and North Cameroon provinces 44(6) (pp. 845-857) https://doi.org/10.1016/S0196-8904(02)00092-4
  12. Celik (2004) A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey (pp. 593-604) https://doi.org/10.1016/j.renene.2003.07.002
  13. Nkongho et al. (2016) Wind energy potential assessment of Cameroon’s coastal regions for the installation of an onshore wind farm https://doi.org/10.1016/j.heliyon.2016.e00187
  14. Hennessey (1977) Some aspects of wind power statistics 16(2) (pp. 119-128) https://doi.org/10.1175/1520-0450(1977)016<0119:SAOWPS>2.0.CO;2
  15. Corotis et al. (1978) Probability models of wind velocity magnitude and persistence 20(6) (pp. 483-493) https://doi.org/10.1016/0038-092X(78)90065-8
  16. Justus et al. (1978) Methods for estimating wind speed frequency distributions 17(3) (pp. 350-353) https://doi.org/10.1175/1520-0450(1978)017<0350:MFEWSF>2.0.CO;2
  17. Rehman et al. (1994) Weibull parameters for wind speed distribution in Saudi Arabia 53(6) (pp. 473-479) https://doi.org/10.1016/0038-092X(94)90126-M
  18. Jamil et al. (1995) Wind power statistics and an evaluation of wind energy density 6(5) (pp. 623-628) https://doi.org/10.1016/0960-1481(95)00041-H
  19. Garcia et al. (1998) Fitting wind speed distributions: a case study 62(2) (pp. 139-144) https://doi.org/10.1016/S0038-092X(97)00116-3
  20. Ivana et al. (2017) Application of four probability distributions for wind speed modelling (pp. 713-718) https://doi.org/10.1016/j.proeng.2017.06.123
  21. Morgan et al. (2011) Probability distributions for offshore wind speeds (pp. 15-26) https://doi.org/10.1016/j.enconman.2010.06.015
  22. Yong et al. (2012) Empirical analysis of wind power potential at multiple heights for North Dakota wind observation sites (pp. 15-26) https://doi.org/10.3968/j.est.1923847920120401.289
  23. Ouarda et al. (2015) Probability distributions of wind speed in the UAE (pp. 414-434) https://doi.org/10.1016/j.enconman.2015.01.036
  24. Crow and Shimizu (1988) Marcel Dekker
  25. Parravano et al. (2012) The dependence of prestellar core mass distributions on the structure of the parental cloud 754(2) https://doi.org/10.1088/0004-637X/754/2/150
  26. Bernardeau and Kofman (1995) Properties of the cosmological density distribution function (pp. 479-498)
  27. Blasi et al. (1999) Cosmological magnetic field limits in an inhomogeneous Universe 514(2) (pp. L79-L82) https://doi.org/10.1086/311958
  28. Kaoga et al. (2014) Performance analysis of methods for estimating Weibull parameters for wind speed distribution in the district of Maroua https://doi.org/10.4314/jfas.v6i2.3
  29. Kaoga et al. (2014) Assessment of wind energy potential for small scale water pumping systems in the north region of Cameroon 3(1) (pp. 38-46) https://doi.org/10.14419/ijbas.v3i1.1769
  30. Kaoga et al. (2014) Performance assessment of two parameters Weibull distribution methods for wind energy applications in the district of Maroua in Cameroon 17(1) (pp. 39-59)
  31. Kaoga et al. (2014) Comparison of five numerical methods for estimating Weibull parameters for wind energy applications in the District of Kousseri Cameroon 3(1) (pp. 72-87)
  32. Talla et al. (2015) Wind energy assessment at Bafoussam, Cameroon 8(9) https://doi.org/10.5539/jsd.v8n9p106
  33. Kengne et al. (2016) Modeling of characteristics of wind by Weibull distribution and estimation of wind energy in Douala, Littoral Region of Cameroon https://doi.org/10.15680/IJIRSET.2016.0505001
  34. Mfewou et al. (2018) Frontières et dynamiques socio-spatiales en Afrique: une analyse à partir des Frontieres sud-Camerounaises 14(5) (pp. 1857-7431) https://doi.org/10.19044/esj.2018.v14n5p285
  35. Uppala et al. (2005) The ERA-40 re-analysis (pp. 2961-3012) https://doi.org/10.1256/qj.04.176
  36. Hersbach (2018) Operational global reanalysis: progress, future directions and synergies with NWP including updates on the ERA5 production status ECMWF Re-Anal (pp. 1-63)
  37. Manwell et al. (2002) Wiley https://doi.org/10.1002/0470846127
  38. Sunday et al. (2012) Analysis of wind speed data and wind energy potential in three selected locations in south-east Nigeria https://doi.org/10.1186/2251-6832-3-7
  39. Hussain (2020) Assessment of wind characteristics and wind power potential of Gharo, Pakistan https://doi.org/10.1155/2021/8960190
  40. Sumair, M., Aized, T., Gardezi, S.A., Bhutta, M.M., Rehman, S.M., Rehman, S.U.: Application of five continuous distributions and evaluation of wind potential at five stations using normal distribution. Energy Explor. Exploit. 1–26.
  41. https://doi.org/10.1177/0144598720939373
  42. Gradshteyn, I., Ryzhik, I.: Table of Integrals, Series, and Products, 7
  43. th
  44. edn. Elsevier, ISBN-13: 978-0-12-373637-6 (2007)
  45. Heni et al. (2015) Wind power density estimation in the middle of Iraq, Karbala Site 4(4) (pp. 9-15)
  46. Suchel (1983) Quelques remarques à propos de la répartition des pluies au Cameroun durant la période sèche 1969–1973 13(1) (pp. 47-61)
  47. Lienou et al. (2010) Impact de la variabilité climatique sur le régime des cours d’eau du sud-cameroun: climat équatorial 13(1) (pp. 47-61)
  48. Kenfack, T., Tsalefac, M., Haidu, I.: Influence du climat sur les epidemies de meningites a meningocoque dans la plaine du diamare (extremenord cameroun). Geographia Technica, Numéro Spécial (2009)