Using the Bees Algorithm to select the optimal speed parameters for wind turbine generators

Fahmy, Ashraf 2012. Using the Bees Algorithm to select the optimal speed parameters for wind turbine generators. Journal of King Saud University - Computer and Information Sciences 24 (1) , pp. 17-26. 10.1016/j.jksuci.2011.07.001

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Abstract

The Bees Algorithm is a recently developed optimization technique that mimics the foraging behavior of honey bees in nature. This study investigates the use of the Bees Algorithm for the selection of the optimal operating speed parameters for wind power units. Three speed parameters need to be optimized, namely, the rated, cut-in, and cut-off (furling) speed of the turbine. The aim of the optimization process is to maximize the yearly power yield and turbine usage time. The choice of the best parameters depends from the wind frequency distribution at the site of installation. Eleven locations on the coastal areas of Egypt were chosen as case studies. The well-known Particle Swarm Optimization was used as a control optimization algorithm. A popular classical approach based on the manual optimization of the sole rated speed was used as baseline for the comparison of results. The optimization of all the three speed parameters and the use of intelligent optimization techniques represent the novelties of this paper. The study showed that the Bees Algorithm outperformed the other two optimization methods. The proposed algorithm was able to find speed parameters that greatly enhanced the power yield, without compromising the usage time or significantly increasing the capital costs. The comparison between the standard manual optimization method and the two intelligent optimization techniques proved the superiority of the latter ones.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords:	Optimization; Wind turbine generator; Capacity factor; Bees Algorithm; PSO; Swarm intelligence
Publisher:	Elsevier
ISSN:	1319-1578
Last Modified:	29 Apr 2016 03:46
URI:	https://orca.cardiff.ac.uk/id/eprint/69253

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