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Numerical data concerning wind farm layout optimization using differential evolution algorithm at different wind speeds

In this work, the numerical data related to wind turbine micrositing problem is presented. The data is acquired using the differential evolution algorithm (DEA) at different wind speeds. The data obtained through DEA include total dissipated power, cost per installation of unit turbine, and the effi...

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Detalles Bibliográficos
Autores principales: Massan, Shafiq-ur-Rehman, Wagan, Asim Imdad, Shaikh, Muhammad Mujtaba, Shah, Muhammad Saleh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635204/
https://www.ncbi.nlm.nih.gov/pubmed/29034287
http://dx.doi.org/10.1016/j.dib.2017.09.040
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author Massan, Shafiq-ur-Rehman
Wagan, Asim Imdad
Shaikh, Muhammad Mujtaba
Shah, Muhammad Saleh
author_facet Massan, Shafiq-ur-Rehman
Wagan, Asim Imdad
Shaikh, Muhammad Mujtaba
Shah, Muhammad Saleh
author_sort Massan, Shafiq-ur-Rehman
collection PubMed
description In this work, the numerical data related to wind turbine micrositing problem is presented. The data is acquired using the differential evolution algorithm (DEA) at different wind speeds. The data obtained through DEA include total dissipated power, cost per installation of unit turbine, and the efficiency of algorithm after installation of any particular number turbines; and are depicted versus number of turbines. The data provided in this paper can be used directly without having to spend weeks of computational time to simulate the results; and can readily be used for comparison with other existing (Massan et al. [1] and Rajper et al. [2], etc.) and forthcoming algorithms in future.
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spelling pubmed-56352042017-10-13 Numerical data concerning wind farm layout optimization using differential evolution algorithm at different wind speeds Massan, Shafiq-ur-Rehman Wagan, Asim Imdad Shaikh, Muhammad Mujtaba Shah, Muhammad Saleh Data Brief Engineering    In this work, the numerical data related to wind turbine micrositing problem is presented. The data is acquired using the differential evolution algorithm (DEA) at different wind speeds. The data obtained through DEA include total dissipated power, cost per installation of unit turbine, and the efficiency of algorithm after installation of any particular number turbines; and are depicted versus number of turbines. The data provided in this paper can be used directly without having to spend weeks of computational time to simulate the results; and can readily be used for comparison with other existing (Massan et al. [1] and Rajper et al. [2], etc.) and forthcoming algorithms in future. Elsevier 2017-09-22 /pmc/articles/PMC5635204/ /pubmed/29034287 http://dx.doi.org/10.1016/j.dib.2017.09.040 Text en © 2017 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Engineering   
Massan, Shafiq-ur-Rehman
Wagan, Asim Imdad
Shaikh, Muhammad Mujtaba
Shah, Muhammad Saleh
Numerical data concerning wind farm layout optimization using differential evolution algorithm at different wind speeds
title Numerical data concerning wind farm layout optimization using differential evolution algorithm at different wind speeds
title_full Numerical data concerning wind farm layout optimization using differential evolution algorithm at different wind speeds
title_fullStr Numerical data concerning wind farm layout optimization using differential evolution algorithm at different wind speeds
title_full_unstemmed Numerical data concerning wind farm layout optimization using differential evolution algorithm at different wind speeds
title_short Numerical data concerning wind farm layout optimization using differential evolution algorithm at different wind speeds
title_sort numerical data concerning wind farm layout optimization using differential evolution algorithm at different wind speeds
topic Engineering   
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635204/
https://www.ncbi.nlm.nih.gov/pubmed/29034287
http://dx.doi.org/10.1016/j.dib.2017.09.040
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