東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Strong wind shear events and improve...

Walton, Renee Amber.

FindBook

Google Book

Amazon

博客來

Strong wind shear events and improved numerical prediction of the wind turbine rotor layer in an Iowa tall tower network.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Strong wind shear events and improved numerical prediction of the wind turbine rotor layer in an Iowa tall tower network./
作者:	Walton, Renee Amber.
面頁冊數:	54 p.
附註:	Source: Masters Abstracts International, Volume: 54-06.
Contained By:	Masters Abstracts International54-06(E).
標題:	Meteorology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1592462
ISBN:	9781321875980

Strong wind shear events and improved numerical prediction of the wind turbine rotor layer in an Iowa tall tower network.
Walton, Renee Amber.

Strong wind shear events and improved numerical prediction of the wind turbine rotor layer in an Iowa tall tower network. - 54 p.

Source: Masters Abstracts International, Volume: 54-06.

Thesis (M.S.)--Iowa State University, 2015.

Day-ahead bids of wind farm power production depend greatly on the accuracy of wind speed forecasts. Forecasts can be improved by expanding knowledge of the wind characteristics across the wind turbine rotor layer (40 - 120 m) and examining wind direction forecasts, as errors in these forecasts can lead to missed effects of wind turbine wakes. Several high shear events with a change in wind speed of up to 15 m s-1 and changes in wind direction up to 30° between 50 and 200 m were observed across an Iowa tall tower network. The strength of these events could lead to damage of wind turbine components and therefore are important to forecast accurately. A six member Weather Research and Forecasting ensemble forecast was developed to evaluate the ability of the model to forecast wind speed, wind direction, wind shear, and stability at several levels across the rotor layer. Four bias correction methods were tested for each parameter to determine the best forecast method. After correction, wind speed forecasts were improved by up to 19%.

ISBN: 9781321875980Subjects--Topical Terms:

542822
Meteorology.

Strong wind shear events and improved numerical prediction of the wind turbine rotor layer in an Iowa tall tower network.
LDR:01938nmm a2200265 4500 001 2115802
005 20170417071245.5
008 180830s2015 ||||||||||||||||| ||eng d
020 $a 9781321875980
035 $a (MiAaPQ)AAI1592462
035 $a AAI1592462
040 $a MiAaPQ $c MiAaPQ
100 1 $a Walton, Renee Amber. $3 3277475
245 1 0 $a Strong wind shear events and improved numerical prediction of the wind turbine rotor layer in an Iowa tall tower network.
300 $a 54 p.
500 $a Source: Masters Abstracts International, Volume: 54-06.
500 $a Advisers: Eugene S. Takle; William A. Gallus Jr.
502 $a Thesis (M.S.)--Iowa State University, 2015.
520 $a Day-ahead bids of wind farm power production depend greatly on the accuracy of wind speed forecasts. Forecasts can be improved by expanding knowledge of the wind characteristics across the wind turbine rotor layer (40 - 120 m) and examining wind direction forecasts, as errors in these forecasts can lead to missed effects of wind turbine wakes. Several high shear events with a change in wind speed of up to 15 m s-1 and changes in wind direction up to 30° between 50 and 200 m were observed across an Iowa tall tower network. The strength of these events could lead to damage of wind turbine components and therefore are important to forecast accurately. A six member Weather Research and Forecasting ensemble forecast was developed to evaluate the ability of the model to forecast wind speed, wind direction, wind shear, and stability at several levels across the rotor layer. Four bias correction methods were tested for each parameter to determine the best forecast method. After correction, wind speed forecasts were improved by up to 19%.
590 $a School code: 0097.
650 4 $a Meteorology. $3 542822
690 $a 0557
710 2 $a Iowa State University. $b Geological and Atmospheric Sciences. $3 3183236
773 0 $t Masters Abstracts International $g 54-06(E).
790 $a 0097
791 $a M.S.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1592462