Wind energy potential assessment based on wind speed, its direction and power data

Abstract Based on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed. Considering the correlation existing and the effect between wind speed and direction, the angular-linear modeling approach is adopted to c...

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Autores principales:	Zhiming Wang, Weimin Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3a371554f18448908043ba3abaefc88d
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spelling	oai:doaj.org-article:3a371554f18448908043ba3abaefc88d2021-12-02T18:51:42ZWind energy potential assessment based on wind speed, its direction and power data10.1038/s41598-021-96376-72045-2322https://doaj.org/article/3a371554f18448908043ba3abaefc88d2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96376-7https://doaj.org/toc/2045-2322Abstract Based on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed. Considering the correlation existing and the effect between wind speed and direction, the angular-linear modeling approach is adopted to construct the joint probability density function of wind speed and direction. For modeling the distribution of wind power density and estimating model parameters of null or low wind speed and multimodal wind speed data, based on expectation–maximization algorithm, a two-component three-parameter Weibull mixture distribution is chosen as wind speed model, and a von Mises mixture distribution with nine components and six components are selected as the models of wind direction and the correlation circular variable between wind speed and direction, respectively. A comprehensive technique of model selection, which includes Akaike information criterion, Bayesian information criterion, the coefficient of determination R 2 and root mean squared error, is used to select the optimal model in all candidate models. The proposed method is applied to averaged 10-min field monitoring wind data and compared with the other estimation methods and judged by the values of R 2 and root mean squared error, histogram plot and wind rose diagram. The results show that the proposed method is effective and the area under study is not suitable for wide wind turbine applications, and the estimated wind energy potential would be inaccuracy without considering the influence of wind direction.Zhiming WangWeimin LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhiming Wang Weimin Liu Wind energy potential assessment based on wind speed, its direction and power data
description	Abstract Based on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed. Considering the correlation existing and the effect between wind speed and direction, the angular-linear modeling approach is adopted to construct the joint probability density function of wind speed and direction. For modeling the distribution of wind power density and estimating model parameters of null or low wind speed and multimodal wind speed data, based on expectation–maximization algorithm, a two-component three-parameter Weibull mixture distribution is chosen as wind speed model, and a von Mises mixture distribution with nine components and six components are selected as the models of wind direction and the correlation circular variable between wind speed and direction, respectively. A comprehensive technique of model selection, which includes Akaike information criterion, Bayesian information criterion, the coefficient of determination R 2 and root mean squared error, is used to select the optimal model in all candidate models. The proposed method is applied to averaged 10-min field monitoring wind data and compared with the other estimation methods and judged by the values of R 2 and root mean squared error, histogram plot and wind rose diagram. The results show that the proposed method is effective and the area under study is not suitable for wide wind turbine applications, and the estimated wind energy potential would be inaccuracy without considering the influence of wind direction.
format	article
author	Zhiming Wang Weimin Liu
author_facet	Zhiming Wang Weimin Liu
author_sort	Zhiming Wang
title	Wind energy potential assessment based on wind speed, its direction and power data
title_short	Wind energy potential assessment based on wind speed, its direction and power data
title_full	Wind energy potential assessment based on wind speed, its direction and power data
title_fullStr	Wind energy potential assessment based on wind speed, its direction and power data
title_full_unstemmed	Wind energy potential assessment based on wind speed, its direction and power data
title_sort	wind energy potential assessment based on wind speed, its direction and power data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3a371554f18448908043ba3abaefc88d
work_keys_str_mv	AT zhimingwang windenergypotentialassessmentbasedonwindspeeditsdirectionandpowerdata AT weiminliu windenergypotentialassessmentbasedonwindspeeditsdirectionandpowerdata
_version_	1718377378303967232

Wind energy potential assessment based on wind speed, its direction and power data

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