scholarly journals Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument

2018 ◽  
Vol 57 (4) ◽  
pp. 2299-2311 ◽  
Author(s):  
Prem Kumar Chaurasiya ◽  
Siraj Ahmed ◽  
Vilas Warudkar
Keyword(s):  
Weibull Distribution ◽  
Wind Power ◽  
Power Density ◽  
Parameters Estimation ◽  
Estimation Methods ◽  
Wind Power Density ◽  
Doppler Sodar

Assessing different parameters estimation methods of Weibull distribution to compute wind power density

2016 ◽  
Vol 108 ◽  
pp. 322-335 ◽  
Author(s):  
Kasra Mohammadi ◽  
Omid Alavi ◽  
Ali Mostafaeipour ◽  
Navid Goudarzi ◽  
Mahdi Jalilvand
Keyword(s):  
Weibull Distribution ◽  
Wind Power ◽  
Power Density ◽  
Parameters Estimation ◽  
Estimation Methods ◽  
Wind Power Density

scholarly journals Assessment of Wind Power Density Based on Weibull Distribution in Region of Junin, Peru

2020 ◽  
Vol 12 (01) ◽  
pp. 16-27
Author(s):  
Jose Galarza ◽  
David Condezo ◽  
Becquer Camayo ◽  
Enrique Mucha
Keyword(s):  
Weibull Distribution ◽  
Wind Power ◽  
Power Density ◽  
Wind Power Density

scholarly journals Investigation of Wind Energy Potential at Selected Locations in the Volta Region of Ghana

2020 ◽  
Vol 1 (1) ◽  
pp. 12-18
Author(s):  
Gabriel Nii Laryea ◽  
Kwabena A. Otu-Danquah
Keyword(s):  
Power Systems ◽  
Wind Energy ◽  
Weibull Distribution ◽  
Wind Power ◽  
Power Density ◽  
International System ◽  
Small Scale ◽  
Wind Power Density ◽  
Wind Potential ◽  
Volta Region

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.

Correction of surface characteristics to diagnostic wind modeling and its impact on potentials of 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 ◽  
...  
Keyword(s):  
Wind Power ◽  
Power Density ◽  
Surface Characteristics ◽  
Wind Power Density ◽  
Wind Modeling

scholarly journals Combining QuickSCAT wind data and Landsat ETM+ images to evaluate the offshore wind power resource of East Vietnam Sea

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
Keyword(s):  
Power Generation ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Geographical Location ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Wind Data ◽  
Power Capacity ◽  
Wind Power Density

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.

scholarly journals Assessment of Wind Energy for Nevada Using Towers and Mesoscale Modeling

2007 ◽  
Author(s):  
Darko Koracin ◽  
Richard L. Reinhardt ◽  
Marshall B. Liddle ◽  
Travis McCord ◽  
Domagoj Podnar ◽  
...  
Keyword(s):  
High Resolution ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Mesoscale Model ◽  
Horizontal Resolution ◽  
Mesoscale Modeling ◽  
Annual Cycles ◽  
Wind Power Density ◽  
Mountainous Regions

The main objectives of the study were to support wind energy assessment for all of Nevada by providing two annual cycles of high-resolution mesoscale modeling evaluated by data from surface stations and towers, estimating differences between these annual cycles and standard wind maps, and providing wind and wind power density statistics at elevations relevant to turbine operations. In addition to the 65 existing Remote Automated Weather Stations in Nevada, four 50-m-tall meteorological towers were deployed in western Nevada to capture long-term wind characteristics and provide database input to verify and improve modeling results. The modeling methodology using Mesoscale Model 5 (MM5) was developed to provide wind and wind power density estimates representing mesoscale effects that include actual synoptic forcing during the two annual cycles (horizontal resolution on the order of 2 and 3 km). The results from the two annual simulation cycles show similar wind statistics with an average difference of less than 100 W/m2. The available TrueWind results for the wind power density at 50 m show greater values of wind power density compared to both MM5-simulated annual cycles for most of the area. However, mainly in the Sierras and the mountainous regions of southern and eastern Nevada, the MM5 simulations indicate greater values for wind power density. The results of this study suggest that the synthesis of the data from a network of tower observations and high-resolution mesoscale modeling is a crucial tool for assessing the wind power density in Nevada and, more generally, other topographically developed areas.

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