Volume 7, Issue 3, May 2018, Page: 33-41
Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia
Tom Wanjekeche, Department of Electrical Engineering, University of Namibia, Ongwediva, Namibia
Received: Jun. 13, 2018;       Accepted: Aug. 3, 2018;       Published: Aug. 30, 2018
DOI: 10.11648/j.epes.20180703.12      View  670      Downloads  90
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.
Wind Turbine, Optimization, Genetic Algorithm, Particle Swarm Optimization, Optimal Arrangement
To cite this article
Tom Wanjekeche, Investigation into the Optimal Wind Turbine Layout Patterns for a Wind Farm in Walvis Bay, Namibia, American Journal of Electrical Power and Energy Systems. Vol. 7, No. 3, 2018, pp. 33-41. doi: 10.11648/j.epes.20180703.12
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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