On Some Models for Wind Power Assessment in Yola, Nigeria

Gongsin Isaac Esbond; Funmilayo Westnand Oshogboye Saporu

doi:doi:10.11648/j.ijsd.20210704.14

| Peer-Reviewed

On Some Models for Wind Power Assessment in Yola, Nigeria

Gongsin Isaac Esbond, Funmilayo Westnand Oshogboye Saporu

Published in International Journal of Statistical Distributions and Applications (Volume 7, Issue 4)

Received: 25 September 2021 Accepted: 21 October 2021 Published: 23 November 2021

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Probability distributions are used in the evaluation of wind energy potentials to describe the wind speed characteristics of the chosen location for wind farm establishment. However, the Weibull distribution that is the most chosen by wind energy modelers may likely fail to properly describe the wind speed data of certain locations, or it may not be the best model to describe wind speed when compared to the fitness of other probability distributions. Thus, in this study, four probability distributions are fitted to wind speed data from Yola, Nigeria. They are the Weibull, the exponentiated Weibull, the generalized power Weibull and the exponentiated epsilon distributions; and, all provided good fit to the wind speed dataset. The exponentiated epsilon distribution is new and provided the best fit. These models are compared based on the relative likelihood gain per data point; it is found that there is about 5% gain by the other three probability distributions over the Weibull distribution. Hence all the three distributions can also be used as wind models. The estimated average wind speeds computed using the four models at various hub heights show that wind is sufficiently available to support a wind turbine with a cut-in speed of 3 m/s at hub heights 90 m above ground level. For the exponentiated-epsilon model, average wind speed of 3.68 m/s at hub height of 120 m above ground level can generate 6.11 W/m² of electricity; and for a wind turbine of rotor diameter of 128 m with 12,868 m² swept area, this amounts to 78.6 kW of electricity supply for a small-scale wind power project. Consequently, Yola holds a good potential for the establishment of a wind farm.

Published in	International Journal of Statistical Distributions and Applications (Volume 7, Issue 4)
DOI	10.11648/j.ijsd.20210704.14
Page(s)	102-107
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Cut-in Wind Speed, Exponentiated-Epsilon Distribution, Likelihood Gain, Turbine Hub Height, Wind Energy, Wind Farm

References

[1]	Augustine, C., & Nnabuchi, M. (2009) Relationship between global solar radiation and sunshine hours for Calabar, Port Harcourt and Enugu, Nigeria. International Journal of Physical Sciences 2009; 4 (4): 182–8.
[2]	Bañuelos-Ruedas, F., Angeles-Camacho, C. & Rios-Marcuello, S. (2011). Methodologies Used in the Extrapolation of Wind Speed Data at Different Heights and Its Impact in the Wind Energy Resource Assessment in a Region, Wind Farm - Technical Regulations, Potential Estimation and Siting Assessment, Dr. GastÃn Orlando Suvire (Ed.), ISBN: 978-953-307-483-2, InTech, Available from: cdn.intechweb.org›pdfs.
[3]	Betz, A., The Maximum of the Theoretically Possible Exploitation of Wind by Means of a Wind Motor, In Wind Engineering, 37, No. 4, 441-446, 2013.
[4]	BP energy outlook 2016. [cited 2016 Dec 14]. Available from: https://www.bp.com/content/dam/bp/pdf/energy-economics/energy-outlook-2016/bp-energy-outlook-2016.pdf. Accessed March 23, 2021.
[5]	BP Statistical Review of World Energy 2019 \| 68th edition, @ https://www.bp.com › bp › global› corporate › pdfs › energy-economics. Accessed March 23, 2021.
[6]	Chen, T., Hausladen, A., Sstamler, J., Granger, D., Khanand, A. H., Cheng, J., Chen, C., Kyriakos, C. A. (2019) A Wind Climate Dynamic Modeling and Control Using Weibull and Extreme Value Distribution System. Fluid Mechanics, 2019; 5 (1): 8-14, doi: 10.11648/j.fm.20190501.12, http://www.sciencepublishinggroup.com/j/fm.
[7]	de Souza, A., Olaofe, Z., Kodicherla, S. P. K., Ikefuti, P., Nobrega, L. & Sabbah, I. (2018) Probability distributions assessment for modeling gas concentration in campo grande, ms, Brazil. eur. chem. bull., 2018, 6 (12), 569-578, doi: 10.17628/ecb.2017.6.569-578.
[8]	Drobinski, P. & Coulais, C. (2012) Is the Weibull distribution really suited for wind statistics modeling and wind power evaluation? In: Wind statistics modeling and wind power evaluation. Available @ https://www.researchgate.net/publication/233427018.
[9]	Gongsin, I. E. & Saporu, W. O. F. (2018), Wind Speed Modeling for Informed Asthma Management in Maiduguri, Borno State, Nigeria. A paper presented in the International Conference on Mathematical Modeling of Environmental Pollution, December 2–6, 2018 at the National Mathematical Centre, Abuja, Nigeria.
[10]	Gongsin, I. E. & Saporu, W. O. F. (2019) Solar Energy Potential in Yola, Adamawa State, Nigeria. International Journal of Renewable Energy Sources, Volume 4, pp 48 – 55. http://www.iaras.org/iaras/journals/ijres.
[11]	Jamdade, P. G. & Jamdade, S. G. (2015) Evaluation of Wind Energy Potential for Four Sites in Ireland using the Weibull Distribution Model. Journal of Power Technologies 95 (1) 48–53.
[12]	Jourdier, B. & Drobinski, P. (2017) Errors in wind resource and energy yield assessments based on the Weibull distribution. Ann. Geophys., 35, 691–700, 2017, doi: 10.5194/angeo-35-691-2017, www.ann-geophys.net/35/691/2017/.
[13]	Justus, C. G., Hargraves, W. R. & Yakin, A. (1976). Nationwide assessment of potential output from wind powered generators, J. Applied Meteorology, 15; 673-678.
[14]	Kehinde, O., Babaremu, K. O., Akpanyung, K. V., Remilekun, E., Oyedele, S. T. & Oluwafemi, J. (2018) Renewable Energy in Nigeria - A Review, International Journal of Mechanical Engineering and Technology 9 (10), 2018, pp. 1085–1094. http://www.iaeme.com/IJMET/index.asp.
[15]	Kumar, M. B. H., Saravanan, B., Sanjeevikumar, P & Holm-Nielsen, J. B (2019) Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India. Energies. 2019, 12, 2158; doi: 10.3390/en12112158. www.mdpi.com/journal/energies.
[16]	Morgan, E. C., Lackner, M., Vogel, R. M. & Baise, L. G. (2011) Probability distributions for offshore wind speeds. Energy Conversion and Management; 52: 15-26.
[17]	Mudholkar, G, S. & Srivastava, D. K. (1993). Exponentiated Weibull Family for Analyzing Bathtub Failure Rate. IEEE Transaction on Reliability, 42, pp 299–302.
[18]	Nikulin, M., & Haghighi, F. (2006). A Chi-squared test for the generalized power Weibull family for the head-and-neck cancer censored data. Journal of Mathematical Sciences, 133 (3), 1333–1341.
[19]	Oguntunde, P. E., Odetunmibi, O. A., & Adejumo, A. O. (2014) A Study of Probability Models in Monitoring Environmental Pollution in Nigeria. Journal of Probability and Statistics, volume 2014, Article ID 864965, 6 pages http://dx.doi.org/10.1155/2014/864965.
[20]	Olusola, B., Mustafa, D., Akinola, B. & Oluwaseun, A. (2017) A review of renewable energy potential in Nigeria; solar power development over the years. Engineering and Applied Science Research, 44 (4): 242-248. https://www.researchgate.net/publication/321384759.
[21]	Ouarda, T. B. M. J., Charron, C., Shin, J. Y., Marpu, P. R., Al-Mandoos, A. H., Al-Tamimi, M. H., Ghedira H., Al Hosary, T. N. (2015) Probability distributions of wind speed in the UAE. Energy Conversion and Management 93 (2015) 414–434. http://creativecommons.org/licenses/by-nc-nd/4.0/.
[22]	Pobočíkováa, I., Sedliačkováa, Z. & Michalková, M. (2017) Application of four probability distributions for wind speed modeling. Procedia Engineering 192 (2017) 713–718. www.sciencedirect.com.
[23]	Saporu, F. W. O. & Gongsin, I. E. (2017), Wind Energy Potential of Maiduguri, Borno State, Nigeria. International Journal of Science and Research, vol. 6, Issue 8, pp 1020–1024, doi: 10.21275/ART20175442. www.ijsr.net.
[24]	Shaaban, M. & Petinrin, J. O. (2014) Renewable energy potentials in Nigeria: Meeting rural energy needs. Renewable and Sustainable Energy Reviews 29 (2014) 72–84. http://dx.doi.org/10.1016/j.rser.2013.08.078.
[25]	Shittu, O. I. & Adepoju, K. A. (2014) On the Exponentiated Weibull Distribution for Modeling Wind Speed in South Western Nigeria. Journal of Modern Applied Statistical Methods, Vol. 13, No. 1, Article 28, pp 431-445. DOI: 10.22237/jmasm/1398918420, Available at: http://digitalcommons.wayne.edu/jmasm/vol13/iss1/28.
[26]	Sohoni, V., Gupta, S., Nema, R. (2016) A comparative analysis of wind speed probability distributions for wind power assessment of four sites. Turkish Journal of Electrical Engineering & Computer Sciences, 24: 4724–4735. https://journals.tubitak.gov.tr/elektrik/.
[27]	Xue, H., Liu, H., Peng, H., Luo Y. & Lin, K. (2019) Wind load and structural parameter estimation from Incomplete Measurements, Hindawi, volume 2019, pp., https://doi.org/10.1155/2019/4862983.
[28]	Zhang, L., Li, Q., Guo, Y., Yang, Z. & Zhang, L. (2018) An investigation of wind direction and speed in a featured wind farm using joint probability distribution methods. Sustainability 10, 4338.

Cite This Article

Plain Text BibTeX RIS

APA Style

Gongsin Isaac Esbond, Funmilayo Westnand Oshogboye Saporu. (2021). On Some Models for Wind Power Assessment in Yola, Nigeria. International Journal of Statistical Distributions and Applications, 7(4), 102-107. https://doi.org/10.11648/j.ijsd.20210704.14

Copy | Download

ACS Style

Gongsin Isaac Esbond; Funmilayo Westnand Oshogboye Saporu. On Some Models for Wind Power Assessment in Yola, Nigeria. Int. J. Stat. Distrib. Appl. 2021, 7(4), 102-107. doi: 10.11648/j.ijsd.20210704.14

Copy | Download

AMA Style

Gongsin Isaac Esbond, Funmilayo Westnand Oshogboye Saporu. On Some Models for Wind Power Assessment in Yola, Nigeria. Int J Stat Distrib Appl. 2021;7(4):102-107. doi: 10.11648/j.ijsd.20210704.14

Copy | Download

@article{10.11648/j.ijsd.20210704.14,
  author = {Gongsin Isaac Esbond and Funmilayo Westnand Oshogboye Saporu},
  title = {On Some Models for Wind Power Assessment in Yola, Nigeria},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {7},
  number = {4},
  pages = {102-107},
  doi = {10.11648/j.ijsd.20210704.14},
  url = {https://doi.org/10.11648/j.ijsd.20210704.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20210704.14},
  abstract = {Probability distributions are used in the evaluation of wind energy potentials to describe the wind speed characteristics of the chosen location for wind farm establishment. However, the Weibull distribution that is the most chosen by wind energy modelers may likely fail to properly describe the wind speed data of certain locations, or it may not be the best model to describe wind speed when compared to the fitness of other probability distributions. Thus, in this study, four probability distributions are fitted to wind speed data from Yola, Nigeria. They are the Weibull, the exponentiated Weibull, the generalized power Weibull and the exponentiated epsilon distributions; and, all provided good fit to the wind speed dataset. The exponentiated epsilon distribution is new and provided the best fit. These models are compared based on the relative likelihood gain per data point; it is found that there is about 5% gain by the other three probability distributions over the Weibull distribution. Hence all the three distributions can also be used as wind models. The estimated average wind speeds computed using the four models at various hub heights show that wind is sufficiently available to support a wind turbine with a cut-in speed of 3 m/s at hub heights 90 m above ground level. For the exponentiated-epsilon model, average wind speed of 3.68 m/s at hub height of 120 m above ground level can generate 6.11 W/m2 of electricity; and for a wind turbine of rotor diameter of 128 m with 12,868 m2 swept area, this amounts to 78.6 kW of electricity supply for a small-scale wind power project. Consequently, Yola holds a good potential for the establishment of a wind farm.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - On Some Models for Wind Power Assessment in Yola, Nigeria
AU  - Gongsin Isaac Esbond
AU  - Funmilayo Westnand Oshogboye Saporu
Y1  - 2021/11/23
PY  - 2021
N1  - https://doi.org/10.11648/j.ijsd.20210704.14
DO  - 10.11648/j.ijsd.20210704.14
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 102
EP  - 107
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20210704.14
AB  - Probability distributions are used in the evaluation of wind energy potentials to describe the wind speed characteristics of the chosen location for wind farm establishment. However, the Weibull distribution that is the most chosen by wind energy modelers may likely fail to properly describe the wind speed data of certain locations, or it may not be the best model to describe wind speed when compared to the fitness of other probability distributions. Thus, in this study, four probability distributions are fitted to wind speed data from Yola, Nigeria. They are the Weibull, the exponentiated Weibull, the generalized power Weibull and the exponentiated epsilon distributions; and, all provided good fit to the wind speed dataset. The exponentiated epsilon distribution is new and provided the best fit. These models are compared based on the relative likelihood gain per data point; it is found that there is about 5% gain by the other three probability distributions over the Weibull distribution. Hence all the three distributions can also be used as wind models. The estimated average wind speeds computed using the four models at various hub heights show that wind is sufficiently available to support a wind turbine with a cut-in speed of 3 m/s at hub heights 90 m above ground level. For the exponentiated-epsilon model, average wind speed of 3.68 m/s at hub height of 120 m above ground level can generate 6.11 W/m2 of electricity; and for a wind turbine of rotor diameter of 128 m with 12,868 m2 swept area, this amounts to 78.6 kW of electricity supply for a small-scale wind power project. Consequently, Yola holds a good potential for the establishment of a wind farm.
VL  - 7
IS  - 4
ER  -

Copy | Download

Author Information

Gongsin Isaac Esbond

Department of Mathematical Sciences, University of Maiduguri, Maiduguri, Nigeria
Funmilayo Westnand Oshogboye Saporu

National Mathematical Centre, Kwali, Abuja, Nigeria

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Gongsin Isaac Esbond, Funmilayo Westnand Oshogboye Saporu. (2021). On Some Models for Wind Power Assessment in Yola, Nigeria. International Journal of Statistical Distributions and Applications, 7(4), 102-107. https://doi.org/10.11648/j.ijsd.20210704.14

Copy | Download

ACS Style

Gongsin Isaac Esbond; Funmilayo Westnand Oshogboye Saporu. On Some Models for Wind Power Assessment in Yola, Nigeria. Int. J. Stat. Distrib. Appl. 2021, 7(4), 102-107. doi: 10.11648/j.ijsd.20210704.14

Copy | Download

AMA Style

Gongsin Isaac Esbond, Funmilayo Westnand Oshogboye Saporu. On Some Models for Wind Power Assessment in Yola, Nigeria. Int J Stat Distrib Appl. 2021;7(4):102-107. doi: 10.11648/j.ijsd.20210704.14

Copy | Download

@article{10.11648/j.ijsd.20210704.14,
  author = {Gongsin Isaac Esbond and Funmilayo Westnand Oshogboye Saporu},
  title = {On Some Models for Wind Power Assessment in Yola, Nigeria},
  journal = {International Journal of Statistical Distributions and Applications},
  volume = {7},
  number = {4},
  pages = {102-107},
  doi = {10.11648/j.ijsd.20210704.14},
  url = {https://doi.org/10.11648/j.ijsd.20210704.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20210704.14},
  abstract = {Probability distributions are used in the evaluation of wind energy potentials to describe the wind speed characteristics of the chosen location for wind farm establishment. However, the Weibull distribution that is the most chosen by wind energy modelers may likely fail to properly describe the wind speed data of certain locations, or it may not be the best model to describe wind speed when compared to the fitness of other probability distributions. Thus, in this study, four probability distributions are fitted to wind speed data from Yola, Nigeria. They are the Weibull, the exponentiated Weibull, the generalized power Weibull and the exponentiated epsilon distributions; and, all provided good fit to the wind speed dataset. The exponentiated epsilon distribution is new and provided the best fit. These models are compared based on the relative likelihood gain per data point; it is found that there is about 5% gain by the other three probability distributions over the Weibull distribution. Hence all the three distributions can also be used as wind models. The estimated average wind speeds computed using the four models at various hub heights show that wind is sufficiently available to support a wind turbine with a cut-in speed of 3 m/s at hub heights 90 m above ground level. For the exponentiated-epsilon model, average wind speed of 3.68 m/s at hub height of 120 m above ground level can generate 6.11 W/m2 of electricity; and for a wind turbine of rotor diameter of 128 m with 12,868 m2 swept area, this amounts to 78.6 kW of electricity supply for a small-scale wind power project. Consequently, Yola holds a good potential for the establishment of a wind farm.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - On Some Models for Wind Power Assessment in Yola, Nigeria
AU  - Gongsin Isaac Esbond
AU  - Funmilayo Westnand Oshogboye Saporu
Y1  - 2021/11/23
PY  - 2021
N1  - https://doi.org/10.11648/j.ijsd.20210704.14
DO  - 10.11648/j.ijsd.20210704.14
T2  - International Journal of Statistical Distributions and Applications
JF  - International Journal of Statistical Distributions and Applications
JO  - International Journal of Statistical Distributions and Applications
SP  - 102
EP  - 107
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20210704.14
AB  - Probability distributions are used in the evaluation of wind energy potentials to describe the wind speed characteristics of the chosen location for wind farm establishment. However, the Weibull distribution that is the most chosen by wind energy modelers may likely fail to properly describe the wind speed data of certain locations, or it may not be the best model to describe wind speed when compared to the fitness of other probability distributions. Thus, in this study, four probability distributions are fitted to wind speed data from Yola, Nigeria. They are the Weibull, the exponentiated Weibull, the generalized power Weibull and the exponentiated epsilon distributions; and, all provided good fit to the wind speed dataset. The exponentiated epsilon distribution is new and provided the best fit. These models are compared based on the relative likelihood gain per data point; it is found that there is about 5% gain by the other three probability distributions over the Weibull distribution. Hence all the three distributions can also be used as wind models. The estimated average wind speeds computed using the four models at various hub heights show that wind is sufficiently available to support a wind turbine with a cut-in speed of 3 m/s at hub heights 90 m above ground level. For the exponentiated-epsilon model, average wind speed of 3.68 m/s at hub height of 120 m above ground level can generate 6.11 W/m2 of electricity; and for a wind turbine of rotor diameter of 128 m with 12,868 m2 swept area, this amounts to 78.6 kW of electricity supply for a small-scale wind power project. Consequently, Yola holds a good potential for the establishment of a wind farm.
VL  - 7
IS  - 4
ER  -

Copy | Download