scholarly journals New estimation method of wind power density with three‐parameter Weibull distribution: A case on Central Inner Mongolia suburbs

Wind Energy ◽  
2021 ◽  
Author(s):  
Wenxin Wang ◽  
Kexin Chen ◽  
Yang Bai ◽  
Yu Chen ◽  
Jianwen Wang
2006 ◽  
Vol 30 (2) ◽  
pp. 161-168 ◽  
Author(s):  
Jiang Yingni ◽  
Yuan Xiuling ◽  
Feng Jianmei ◽  
Cheng Xiaojun

2016 ◽  
Vol 108 ◽  
pp. 322-335 ◽  
Author(s):  
Kasra Mohammadi ◽  
Omid Alavi ◽  
Ali Mostafaeipour ◽  
Navid Goudarzi ◽  
Mahdi Jalilvand

2020 ◽  
Vol 12 (01) ◽  
pp. 16-27
Author(s):  
Jose Galarza ◽  
David Condezo ◽  
Becquer Camayo ◽  
Enrique Mucha

2020 ◽  
Vol 1 (1) ◽  
pp. 12-18
Author(s):  
Gabriel Nii Laryea ◽  
Kwabena A. Otu-Danquah

The cost of extending electrical power systems to remote areas is expensive and the option is to decentralise small scale energy power systems that could meet the demand in those areas. Wind potential at selected locations in Ghana has been studied for wind energy purpose since there is an increase in socio-economic development in the country. Four (4) of the three-cup anemometers and data-loggers were installed on a 20-m and 30m masts at Amedzofe, Anloga, Areeba-Nkwanta and Kue-Nkwanta (all in the Volta region). The recorded data were analyzed using STATISTICAL software and calculations were made to obtain the Weibull distribution two-parameters and prediction for hourly, monthly mean wind speeds and mean wind power densities were predicted at 50 m above ground level. Among the selected sites, the mean wind power density of Amedzofe and Anloga fell into Classes 3 and 6 of the commercially international system of wind classification respectively, while Areeba-Nkwanta and Kue-Nkwanta fell into Class 2. Anloga is therefore a suitable site for wind power generation to serve the community. Keywords: Wind power potential; Wind atlas analysis and application program (WAsP); Weibull distribution; Wind speed; Wind power density.


2014 ◽  
Vol 6 (4) ◽  
pp. 042012 ◽  
Author(s):  
Dong Hyeok Kim ◽  
Hwa Woon Lee ◽  
Soon Yeong Park ◽  
Jung Woo Yu ◽  
Changhyoun Park ◽  
...  

2020 ◽  
Vol 20 (2) ◽  
pp. 143-153
Author(s):  
Nguyen Xuan Tung ◽  
Do Huy Cuong ◽  
Bui Thi Bao Anh ◽  
Nguyen Thi Nhan ◽  
Tran Quang Son

Since the East Vietnam Sea has an advantageous geographical location and rich natural resources, we can develop and manage islands and reefs in this region reasonably to declare national sovereignty. Based on 1096 scenes of QuikSCAT wind data of 2006–2009, wind power density at 10 m hight is calculated to evaluate wind energy resources of the East Vietnam Sea. With a combination of wind power density at 70 m hight calculated according to the power law of wind energy profile and reef flats extracted from 35 scenes of Landsat ETM+ images, installed wind power capacity of every island or reef is estimated to evaluate wind power generation of the East Vietnam Sea. We found that the wind power density ranges from levels 4–7, so that the wind energy can be well applied to wind power generation. The wind power density takes on a gradually increasing trend in seasons. Specifically, the wind power density is lower in spring and summer, whereas it is higher in autumn and winter. Among islands and reefs in the East Vietnam Sea, the installed wind power capacity of Hoang Sa archipelago is highest in general, the installed wind power capacity of Truong Sa archipelago is at the third level. The installed wind power capacity of Discovery Reef, Bombay Reef, Tree island, Lincoln island, Woody Island of Hoang Sa archipelago and Mariveles Reef, Ladd Reef, Petley Reef, Cornwallis South Reef of Truong Sa archipelago is relatively high, and wind power generation should be developed on these islands first.


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