Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia

Tom Wanjekeche

doi:doi:10.11648/j.epes.20180703.12

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Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia

Tom Wanjekeche

Published in American Journal of Electrical Power and Energy Systems (Volume 7, Issue 3)

Received: 13 June 2018 Accepted: 3 August 2018 Published: 30 August 2018

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Abstract

Due to the unpredictable nature of wind speed and direction, there is a need to optimize the wind turbines placement to extract maximum available wind power at a low cost. Through optimization, best positions of the wind turbines that lead to maximum output are determined. This paper presents an into an optimal Wind Turbine (WT) layout pattern for three Wind Farm (WF) configurations (aligned, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A Hypothetical WF (2km X 2km) is analyzed based on 2016 Wind data. Result shows that the total power generated from the customized is 863.098 kW, from Genetic Algorithm (GA) layout, the total power generated is 1296.286 kW while from Particle Swarm Optimization (PSO) the total power generated is 1300.668 kW. In comparison to the customized layout, optimization algorithms layouts resulted in a good improvement of the total power generated, GA improved the total power generated by 50.2% while PSO improved the total power generated by 50.7%. Optimization Algorithms layout proved to be efficient as compared to the customized layout because they have fewer power losses. GA and PSO layout have losses of 13.5% and 13.3% respectively, while the customized layout resulted in the most losses which are at 43%. The results from GA and PSO slightly differ, with a small difference in power of 4.4 kW.

Published in	American Journal of Electrical Power and Energy Systems (Volume 7, Issue 3)
DOI	10.11648/j.epes.20180703.12
Page(s)	33-41
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), 2018. Published by Science Publishing Group

Keywords

Wind Turbine, Optimization, Genetic Algorithm, Particle Swarm Optimization, Optimal Arrangement

References

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[2]	P. N. Alireza Emami, “New Approach on Optimazation in Placement of Wind Turbines within Wind Farm by Genetic Algorithm,” Renewable Energy, vol. 35, pp. 1559-1564, 2010.
[3]	G. M. Masters, Renewable and Efﬁcient Electric Power Systems, 2nd Edition, Hoboken, New Jersey: Weley-IEEE Press, 2013.
[4]	A. C. R. Saini, “Statistical Analysis of Wind Speed Data Using Weibull Distribution Parameters,” in 1st International Conference on Non Conventional Energy, Kalyani, 2014.
[5]	C. P. a. B. D. G. Mosetti, “Optimization of wind turbine positioning in large windfarms by means of a genetic algorithm,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 51, no. 1, pp. 105 - 116, 1994.
[6]	I. M. a. D. Borissova, “Wind turbines type and number choice using combinatorial optimization,” Renewable Energy,, vol. 35, no. 9, pp. 1887 - 1894, 2010.
[7]	H. D. S. a. J. C. Smith, “A polyhedral study of the generalized vertex packing problem,” Mathematical Programming, vol. 107, no. 3, pp. 367 - 390, 2005.
[8]	P. R. a. G. L. A. Tessauro, “State of the Art of Wind Farm Optimisation,” DTU Wind Energy.
[9]	J. W. a. X. Z. Chunqui Wan, “Optimal Micro-sitting of Wind Farms by Particle swarm Optimization,” Renewable Energy, vol. 1, no. 47, pp. 198 - 205, 2010.
[10]	P. F. G. C. L. T. B. J. L. a. H. A. M. Pierre-Elouan Réthoré, “TOPFARM: Multi-fidelity optimization of wind farms,” John Wiley & Sons, Ltd., Roskilde, 2013.
[11]	Á. G. R. J. C. M. R. S. a. M. B. P. J. Serrano González, “A New Tool for Wind Farm Optimal Design,” in IEEE Bucharest Power Tech Conference, Bucharest, 2009.
[12]	C. A. P. M. Bryony L. Dupont, “Optimization Of Wind Farm Layout And Wind Turbine Geometry Using A Multilevel Extended Pattern Search Algorithm That Accounts For Variation In Wind Shear Profile Shape,” In Proceedings of The Asme 2012 International Design Engineering Technical Conferences & Computers And Information In Engineering Conference, Chicago, 2012.
[13]	M. H. a. M. A. S. A Grady, “Placement of Wind Turbines using Genetic Algorithms,” Renewable Energy, vol. 30, no. 2, pp. 259 - 270, 2005.
[14]	M. Y. H. A. R. N. R. a. M. N. M. N. Rabia Shakoor, “Wind Farm Layout Optimization by Using Definite Point Selection and Genetic Algorithm,” in IEEE International Conference Power & Energy (PECON), 2014.
[15]	Jun Wang and Xing Zhang, “Wind farm micro-sitting by Gaussian particle swarm optimization with local search strategy,” Renewable Energy, no. 48, pp. 276-, 2012.
[16]	J. W. a. X. Zhang, “Optimal Micro-siting of Wind Farms by Particles Swarm Optimisation,” in Lecture Notes in Computer Science, Beijing, 2010.

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Tom Wanjekeche. (2018). Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia. American Journal of Electrical Power and Energy Systems, 7(3), 33-41. https://doi.org/10.11648/j.epes.20180703.12

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ACS Style

Tom Wanjekeche. Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia. Am. J. Electr. Power Energy Syst. 2018, 7(3), 33-41. doi: 10.11648/j.epes.20180703.12

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AMA Style

Tom Wanjekeche. Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia. Am J Electr Power Energy Syst. 2018;7(3):33-41. doi: 10.11648/j.epes.20180703.12

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@article{10.11648/j.epes.20180703.12,
  author = {Tom Wanjekeche},
  title = {Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {7},
  number = {3},
  pages = {33-41},
  doi = {10.11648/j.epes.20180703.12},
  url = {https://doi.org/10.11648/j.epes.20180703.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20180703.12},
  abstract = {Due to the unpredictable nature of wind speed and direction, there is a need to optimize the wind turbines placement to extract maximum available wind power at a low cost. Through optimization, best positions of the wind turbines that lead to maximum output are determined. This paper presents an into an optimal Wind Turbine (WT) layout pattern for three Wind Farm (WF) configurations (aligned, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A Hypothetical WF (2km X 2km) is analyzed based on 2016 Wind data. Result shows that the total power generated from the customized is 863.098 kW, from Genetic Algorithm (GA) layout, the total power generated is 1296.286 kW while from Particle Swarm Optimization (PSO) the total power generated is 1300.668 kW. In comparison to the customized layout, optimization algorithms layouts resulted in a good improvement of the total power generated, GA improved the total power generated by 50.2% while PSO improved the total power generated by 50.7%. Optimization Algorithms layout proved to be efficient as compared to the customized layout because they have fewer power losses. GA and PSO layout have losses of 13.5% and 13.3% respectively, while the customized layout resulted in the most losses which are at 43%. The results from GA and PSO slightly differ, with a small difference in power of 4.4 kW.},
 year = {2018}
}

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TY  - JOUR
T1  - Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia
AU  - Tom Wanjekeche
Y1  - 2018/08/30
PY  - 2018
N1  - https://doi.org/10.11648/j.epes.20180703.12
DO  - 10.11648/j.epes.20180703.12
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 33
EP  - 41
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20180703.12
AB  - Due to the unpredictable nature of wind speed and direction, there is a need to optimize the wind turbines placement to extract maximum available wind power at a low cost. Through optimization, best positions of the wind turbines that lead to maximum output are determined. This paper presents an into an optimal Wind Turbine (WT) layout pattern for three Wind Farm (WF) configurations (aligned, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A Hypothetical WF (2km X 2km) is analyzed based on 2016 Wind data. Result shows that the total power generated from the customized is 863.098 kW, from Genetic Algorithm (GA) layout, the total power generated is 1296.286 kW while from Particle Swarm Optimization (PSO) the total power generated is 1300.668 kW. In comparison to the customized layout, optimization algorithms layouts resulted in a good improvement of the total power generated, GA improved the total power generated by 50.2% while PSO improved the total power generated by 50.7%. Optimization Algorithms layout proved to be efficient as compared to the customized layout because they have fewer power losses. GA and PSO layout have losses of 13.5% and 13.3% respectively, while the customized layout resulted in the most losses which are at 43%. The results from GA and PSO slightly differ, with a small difference in power of 4.4 kW.
VL  - 7
IS  - 3
ER  -

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Author Information

Tom Wanjekeche

Department of Electrical Engineering, University of Namibia, Ongwediva, Namibia

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Plain Text BibTeX RIS

APA Style

Tom Wanjekeche. (2018). Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia. American Journal of Electrical Power and Energy Systems, 7(3), 33-41. https://doi.org/10.11648/j.epes.20180703.12

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ACS Style

Tom Wanjekeche. Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia. Am. J. Electr. Power Energy Syst. 2018, 7(3), 33-41. doi: 10.11648/j.epes.20180703.12

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AMA Style

Tom Wanjekeche. Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia. Am J Electr Power Energy Syst. 2018;7(3):33-41. doi: 10.11648/j.epes.20180703.12

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@article{10.11648/j.epes.20180703.12,
  author = {Tom Wanjekeche},
  title = {Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia},
  journal = {American Journal of Electrical Power and Energy Systems},
  volume = {7},
  number = {3},
  pages = {33-41},
  doi = {10.11648/j.epes.20180703.12},
  url = {https://doi.org/10.11648/j.epes.20180703.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.epes.20180703.12},
  abstract = {Due to the unpredictable nature of wind speed and direction, there is a need to optimize the wind turbines placement to extract maximum available wind power at a low cost. Through optimization, best positions of the wind turbines that lead to maximum output are determined. This paper presents an into an optimal Wind Turbine (WT) layout pattern for three Wind Farm (WF) configurations (aligned, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A Hypothetical WF (2km X 2km) is analyzed based on 2016 Wind data. Result shows that the total power generated from the customized is 863.098 kW, from Genetic Algorithm (GA) layout, the total power generated is 1296.286 kW while from Particle Swarm Optimization (PSO) the total power generated is 1300.668 kW. In comparison to the customized layout, optimization algorithms layouts resulted in a good improvement of the total power generated, GA improved the total power generated by 50.2% while PSO improved the total power generated by 50.7%. Optimization Algorithms layout proved to be efficient as compared to the customized layout because they have fewer power losses. GA and PSO layout have losses of 13.5% and 13.3% respectively, while the customized layout resulted in the most losses which are at 43%. The results from GA and PSO slightly differ, with a small difference in power of 4.4 kW.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia
AU  - Tom Wanjekeche
Y1  - 2018/08/30
PY  - 2018
N1  - https://doi.org/10.11648/j.epes.20180703.12
DO  - 10.11648/j.epes.20180703.12
T2  - American Journal of Electrical Power and Energy Systems
JF  - American Journal of Electrical Power and Energy Systems
JO  - American Journal of Electrical Power and Energy Systems
SP  - 33
EP  - 41
PB  - Science Publishing Group
SN  - 2326-9200
UR  - https://doi.org/10.11648/j.epes.20180703.12
AB  - Due to the unpredictable nature of wind speed and direction, there is a need to optimize the wind turbines placement to extract maximum available wind power at a low cost. Through optimization, best positions of the wind turbines that lead to maximum output are determined. This paper presents an into an optimal Wind Turbine (WT) layout pattern for three Wind Farm (WF) configurations (aligned, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A Hypothetical WF (2km X 2km) is analyzed based on 2016 Wind data. Result shows that the total power generated from the customized is 863.098 kW, from Genetic Algorithm (GA) layout, the total power generated is 1296.286 kW while from Particle Swarm Optimization (PSO) the total power generated is 1300.668 kW. In comparison to the customized layout, optimization algorithms layouts resulted in a good improvement of the total power generated, GA improved the total power generated by 50.2% while PSO improved the total power generated by 50.7%. Optimization Algorithms layout proved to be efficient as compared to the customized layout because they have fewer power losses. GA and PSO layout have losses of 13.5% and 13.3% respectively, while the customized layout resulted in the most losses which are at 43%. The results from GA and PSO slightly differ, with a small difference in power of 4.4 kW.
VL  - 7
IS  - 3
ER  -

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