A statistical method to construct wind speed at turbine height for study of wind power in China
A statistical method to construct wind speed at turbine height for study of wind power in China
复制标题
我国风电研究中风机高度风速统计方法
DOI：
10.1007/s00704-020-03201-8
复制
发表时间：
2020-04
影响因子：
3.4
通讯作者：
Li X.
中科院分区：
文献类型：
--
作者：
Zhou X.;Qin J.;Li H. D.;Tang W.;Pan X.;Huang B.;Li X.
风电的准确估算不仅对于风能研究至关重要，而且对于改善国家政府的政策和规划决策也至关重要。然而，可用于中国风电估算的可靠风速数据集非常有限。在本研究中，使用无线电探空仪风速测量来优化模拟风廓线（通过幂律方法表示）。然后使用优化的风廓线和在全国每个常规气象站测量的 10 米风速来构建典型涡轮机高度 (80 m) 的风速。与实测值相比，构建的风速具有合理的误差（平均偏差为0.08 m s−1，均方根误差为0.89 m s−1，相关系数为0.90），远小于模型误差。进一步的比较表明，本研究中的构建方法优于其他类似的统计方法。因此，该方法和构建的风速数据集适合我国风能研究。
The accurate estimation of wind power is crucial not only to conduct wind energy research but also to improve policy and planning decisions for the national government. However, reliable wind speed datasets at turbine height that can be used for wind power estimation in China are very limited. In this study, the simulated wind profile (expressed by a power-law approach) is optimized using radiosonde wind speed measurements. The wind speed at a typical turbine height (80 m) is then constructed using the optimized wind profile and 10-m wind speed measured at each conventional meteorological station throughout the country. Compared with measurements, the constructed wind speed has reasonable errors (0.08 m s−1in mean bias, 0.89 m s−1in root mean square error, and 0.90 in correlation coefficient), which are much smaller than the model errors. Further comparison shows the constructing method in the current study outperforms the other similar statistical methods. Therefore, this method and the constructed wind speed dataset are suggested for wind energy research in China.
登录
查看更多内容
影响因子：
3.2
作者：
Feimin Zhang;Yi Yang;Chenghai Wang
通讯作者：
Feimin Zhang;Yi Yang;Chenghai Wang
影响因子：
15.9
作者：
Shengpeng Sun;Fengliang Liu;Song Xue;M. Zeng;Fanxiao Zeng
通讯作者：
Shengpeng Sun;Fengliang Liu;Song Xue;M. Zeng;Fanxiao Zeng
影响因子：
15.9
作者：
Sufang Zhang;Xingmei Li
通讯作者：
Sufang Zhang;Xingmei Li
影响因子：
--
作者：
C. Archer;M. Jacobson
通讯作者：
C. Archer;M. Jacobson
影响因子：
3.4
作者：
Huidong Li;M. Wolter;Xun Wang;S. Sodoudi
通讯作者：
Huidong Li;M. Wolter;Xun Wang;S. Sodoudi