Investigating the possibility of using different hub height wind turbines in a wind farm

Many researchers have focused on the layout design of a wind farm using the computational methods. Most of previous researches focused on relevant large cell size and using same hub height wind turbines. In this paper, the authors investigate the possibility of using different hub height wind turbin...

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Bibliographic Details
Main Authors: Ying Chen, Hua Li, Kai Jin, Yousri Elkassabgi
Format: Article
Language:English
Published: Taylor & Francis Group 2017-02-01
Series:International Journal of Sustainable Energy
Subjects:
Online Access:http://dx.doi.org/10.1080/14786451.2015.1007139
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author Ying Chen
Hua Li
Kai Jin
Yousri Elkassabgi
author_facet Ying Chen
Hua Li
Kai Jin
Yousri Elkassabgi
author_sort Ying Chen
collection DOAJ
description Many researchers have focused on the layout design of a wind farm using the computational methods. Most of previous researches focused on relevant large cell size and using same hub height wind turbines. In this paper, the authors investigate the possibility of using different hub height wind turbines in a wind farm. A limited area (2 km × 2 km) with constant wind speed and direction is considered as the potential wind farm area, and a nested genetic algorithm is used as optimisation algorithm. Two different hub height wind turbines are introduced with two different cell sizes. Power output, cost, payback period, and total profit are selected as evaluation criteria when comparing the layouts with same hub height wind turbines with the layouts with different hub height wind turbines. The results demonstrate that it is feasible and possible to use different hub height wind turbines in a wind farm.
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spelling doaj.art-31fb14df37d44859be32b51eb8cf1a962023-09-20T10:33:44ZengTaylor & Francis GroupInternational Journal of Sustainable Energy1478-64511478-646X2017-02-0136214215010.1080/14786451.2015.10071391007139Investigating the possibility of using different hub height wind turbines in a wind farmYing Chen0Hua Li1Kai Jin2Yousri Elkassabgi3Texas A&M University-KingsvilleTexas A&M University-KingsvilleTexas A&M University-KingsvilleTexas A&M University-KingsvilleMany researchers have focused on the layout design of a wind farm using the computational methods. Most of previous researches focused on relevant large cell size and using same hub height wind turbines. In this paper, the authors investigate the possibility of using different hub height wind turbines in a wind farm. A limited area (2 km × 2 km) with constant wind speed and direction is considered as the potential wind farm area, and a nested genetic algorithm is used as optimisation algorithm. Two different hub height wind turbines are introduced with two different cell sizes. Power output, cost, payback period, and total profit are selected as evaluation criteria when comparing the layouts with same hub height wind turbines with the layouts with different hub height wind turbines. The results demonstrate that it is feasible and possible to use different hub height wind turbines in a wind farm.http://dx.doi.org/10.1080/14786451.2015.1007139wind farmlayout designoptimisationgenetic algorithm
spellingShingle Ying Chen
Hua Li
Kai Jin
Yousri Elkassabgi
Investigating the possibility of using different hub height wind turbines in a wind farm
International Journal of Sustainable Energy
wind farm
layout design
optimisation
genetic algorithm
title Investigating the possibility of using different hub height wind turbines in a wind farm
title_full Investigating the possibility of using different hub height wind turbines in a wind farm
title_fullStr Investigating the possibility of using different hub height wind turbines in a wind farm
title_full_unstemmed Investigating the possibility of using different hub height wind turbines in a wind farm
title_short Investigating the possibility of using different hub height wind turbines in a wind farm
title_sort investigating the possibility of using different hub height wind turbines in a wind farm
topic wind farm
layout design
optimisation
genetic algorithm
url http://dx.doi.org/10.1080/14786451.2015.1007139
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