Cargando…
Wind energy potential assessment based on wind speed, its direction and power data
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...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8377008/ https://www.ncbi.nlm.nih.gov/pubmed/34413418 http://dx.doi.org/10.1038/s41598-021-96376-7 |
_version_ | 1783740570470973440 |
---|---|
author | Wang, Zhiming Liu, Weimin |
author_facet | Wang, Zhiming Liu, Weimin |
author_sort | Wang, Zhiming |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-8377008 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83770082021-08-27 Wind energy potential assessment based on wind speed, its direction and power data Wang, Zhiming Liu, Weimin Sci Rep Article 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. Nature Publishing Group UK 2021-08-19 /pmc/articles/PMC8377008/ /pubmed/34413418 http://dx.doi.org/10.1038/s41598-021-96376-7 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Wang, Zhiming Liu, Weimin Wind energy potential assessment based on wind speed, its direction and power data |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8377008/ https://www.ncbi.nlm.nih.gov/pubmed/34413418 http://dx.doi.org/10.1038/s41598-021-96376-7 |
work_keys_str_mv | AT wangzhiming windenergypotentialassessmentbasedonwindspeeditsdirectionandpowerdata AT liuweimin windenergypotentialassessmentbasedonwindspeeditsdirectionandpowerdata |