A Generalized Approach to Estimating Capacity Factor of Fixed Speed Wind Turbines

2012 ◽  
Vol 3 (3) ◽  
pp. 607-608 ◽  
Author(s):  
Yuri Ditkovich ◽  
Alon Kuperman ◽  
Asher Yahalom ◽  
Michael Byalsky
Keyword(s):  
Wind Turbines ◽  
Capacity Factor

Wind energy resource assessment of Kütahya, Turkey using WAsP and layout optimization

2020 ◽  
pp. 095765092093603
Author(s):  
Onur Koşar ◽  
Mustafa Arif Özgür
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Wind Farm ◽  
Real Life ◽  
Energy Resource ◽  
Resource Assessment ◽  
Capacity Factor ◽  
Strong Wind ◽  
Wind Potential ◽  
Shear Turbulence

Kütahya is considered as a candidate region for a wind farm investment due to Turkey's 2023 energy targets and its proximity to other wind farm investments. In this study, two years of wind data collected from a hill near the Evliya Çelebi Campus of Kütahya Dumlupınar University was used to evaluate the wind farm potential of Kütahya. First, the wind speed, wind direction, wind shear, turbulence intensity and wind speed ramp characteristics were determined. Second, the WAsP software was used to create a wind atlas for the region. Three sites with strong wind potential were evaluated. A techno-economic analysis was conducted using five types of wind turbines selected from the WAsP database. Third, optimization of a wind farm layout was conducted by considering different hub height options for 14 commercial wind turbines using MATLAB software. It was shown theoretically that a wind farm with a power capacity of 25 MW can operate with a capacity factor of 35%. However, due to the relatively high topographical ruggedness index on the wind farm site, the calculated value for the capacity factor could not be reached in a real-life application.

Wind Turbines Capacity Factor Modeling—A Novel Approach

2009 ◽  
Vol 24 (3) ◽  
pp. 1637-1638 ◽  
Author(s):  
M.H. Albadi ◽  
E.F. El-Saadany
Keyword(s):  
Wind Turbines ◽  
Capacity Factor ◽  
Novel Approach ◽  
Factor Modeling

Capacity factor estimation of variable-speed wind turbines considering the coupled influence of the QN-curve and the air density

Energy ◽  
2019 ◽  
Vol 183 ◽  
pp. 1049-1060 ◽  
Author(s):  
Dongran Song ◽  
Yinggang Yang ◽  
Songyue Zheng ◽  
Weiyi Tang ◽  
Jian Yang ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Capacity Factor ◽  
Variable Speed ◽  
Air Density ◽  
Factor Estimation

Capacity factor of wind turbines

Energy ◽  
1997 ◽  
Vol 22 (5) ◽  
pp. 487-491 ◽  
Author(s):  
K.A. Abed ◽  
A.A. El-Mallah
Keyword(s):  
Wind Turbines ◽  
Capacity Factor

scholarly journals Wind Resource Assessment for southern part of Libya: Case Study of Hun

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Hiba Shreif ◽  
W El-Osta ◽  
A Yagub
Keyword(s):  
Wind Energy ◽  
Power Density ◽  
Wind Turbines ◽  
Scale Parameter ◽  
Energy Resource ◽  
Capacity Factor ◽  
Wind Data ◽  
Maximum Speed ◽  
Average Wind Speed ◽  
Wind Resource

The purpose of this study is to analyze the wind energy resource potential at Hun. Wind data was analyzed using different statistical models and calculations were performed to forecast wind energy & power density at the site. Energy production was estimated using different wind turbines which were selected according IEC standards criteria and performance of these wind turbines. Detailed wind resource data analysis was performed for the proposed site using Excel spreadsheet for one-year period from (April 2011 to March 2012). The wind data are measured at four heights of (20 m, 40m, 60m and 61m) above ground level (a.g.l). The analysis showed that the annual average wind speed is 5.69 m/s and the power density is about 190 W/m2 at 61m height. It could be noticed that at 61m height, the highest scale parameter is 7.25 m/s in April while the lowest scale parameter is 5.71 m/s in October. The annual shape and scale parameters range from 2.27 at 61m to 2 at 20m, and from 6.42 m /s at 61m to 5 m /s at 20m, respectively. 90% of the speeds are below 11m/s, 84% are below 10m/s and 50% are above 6 m/s. The maximum speed is 21 m/s with 0.14% occurrence. The wind shear exponent was evaluated as 0.18 and the roughness length for the site as 0.17 m, which indicates that the roughness class for the location is 2.5. According to the performed analysis, the wind turbines suitable for this site should be of class III/B. Comparison of three wind turbines indicated that Vestas V112-3000 gave the highest capacity factor of 42% in April and an availability of 83% while Nordex N100-2500 gave capacity factor of 41% for the same month and availability of 83.7%.

scholarly journals Comparison of the capacity factor of stationary wind turbines and weather-routed energy ships in the far-offshore

2019 ◽  
Vol 1356 ◽  
pp. 012001 ◽  
Author(s):  
Roshamida ABD Jamil ◽  
Alisée Chaigneau ◽  
Jean-Christophe Gilloteaux ◽  
Philippe Lelong ◽  
AuréLien Babarit
Keyword(s):  
Wind Turbines ◽  
Capacity Factor

scholarly journals INFLUENCE OF THE CHANGING LOCAL CLIMATE ON WIND POTENTIALS OF MOUNT KOPAONIK

2017 ◽  
Vol 15 (3) ◽  
pp. 507
Author(s):  
Predrag Živković ◽  
Mladen Tomić ◽  
Dragana Dimitrijević ◽  
Ivana Kecman ◽  
Mirko Dobrnjac
Keyword(s):  
Economic Analysis ◽  
Wind Turbines ◽  
Simulation Software ◽  
Capacity Factor ◽  
Wind Data ◽  
Economic Factors ◽  
Local Climate ◽  
The Real

Obtaining all acceptable locations is one of the main tasks for siting wind turbines. The economic factors are usually very limiting. Very thorough analyses are needed in order to ensure the project finalization. Nevertheless, even after all the steps are made, some problems may occur. One of them is the real status of the winds in the so-called climatology period. This paper focuses on the influences of the changing winds after the preliminary estimations are done. The estimations are obtained using the WAsP simulation software. The results are compared in terms of quality and quantity of the wind data and capacity factor. Finally, an economic analysis is done.

Research on Reliability of Offshore Wind Turbine

2012 ◽  
Vol 443-444 ◽  
pp. 1060-1065
Author(s):  
Tian Yu Liu
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Early Warning ◽  
Drive Train ◽  
Capacity Factor ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Maintenance Time ◽  
Mechanical Dynamics ◽  
Turbine Drive

This paper is concerned with the enhancement of the Capacity Factor for wind turbines, especially offshore. In particular the work describes analysis of data on a wind turbine drive train to enhance the reliability of components, reduce maintenance time and provide early warning of failures from the understanding of mechanical dynamics.

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