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

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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

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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), 2021. Published by Science Publishing Group

Keywords

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

References

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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

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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

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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

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@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}
}

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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  -

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Author Information

Gongsin Isaac Esbond

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

National Mathematical Centre, Kwali, Abuja, Nigeria

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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

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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

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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

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@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}
}

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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  -

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