scholarly journals Effect of Wind Turbine Classes on the Electricity Production of Wind Farms in Cyprus Island

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yiannis A. Katsigiannis ◽  
George S. Stavrakakis ◽  
Christodoulos Pharconides
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Wind Farms ◽  
Electricity Production ◽  
Wind Speeds ◽  
Wind Potential

This paper examines the effect of different wind turbine classes on the electricity production of wind farms in two areas of Cyprus Island, which present low and medium wind potentials: Xylofagou and Limassol. Wind turbine classes determine the suitability of installing a wind turbine in a particulate site. Wind turbine data from five different manufacturers have been used. For each manufacturer, two wind turbines with identical rated power (in the range of 1.5 MW–3 MW) and different wind turbine classes (IEC II and IEC III) are compared. The results show the superiority of wind turbines that are designed for lower wind speeds (IEC III class) in both locations, in terms of energy production. This improvement is higher for the location with the lower wind potential and starts from 7%, while it can reach more than 50%.

scholarly journals Inter-annual variability of wind climates and wind turbine annual energy production

2018 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Standard Deviation ◽  
Wind Energy ◽  
Wind Turbine ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Speeds ◽  
Annual Variability ◽  
Eastern Usa

Abstract. Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind turbine hub-heights is poorly constrained and maybe overestimated by the 6 % standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the long-term wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6 %. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90 % of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

scholarly journals Evaluation of AEP Predictions for Commercial Wind Farms in Sweden

2020 ◽  
Vol 10 (22) ◽  
pp. 7995
Author(s):  
Erik Möllerström ◽  
Daniel Lindholm
Keyword(s):  
South Africa ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Wind Farms ◽  
Project Planning ◽  
Data Availability ◽  
Energy Calculation ◽  
The Mean ◽  
Over Time

Based on data from 1162 wind turbines, with a rated power of at least 1.8 MW, installed in Sweden after 2005, the accuracy of the annual energy production (AEP) predictions from the project planning phases has been compared to the wind-index-corrected production. Both the production and the predicted AEP data come from the database Vindstat, which collects information directly from wind turbine owners. The mean error was 7.1%, which means that, overall, the predicted AEP has been overestimated. The overestimation was higher for wind turbines situated in open terrain than in forest areas and was higher overall than that previously established for the British Isles and South Africa. Dividing the result over the installation year, the improvement which had been expected due to the continuous refinement of the methods and better data availability, was not observed over time. The major uncertainty comes from the predicted AEP as reported by wind turbine owners to the Vindstat database, which, for some cases, might not come from the wind energy calculation from the planning phase (i.e., the P50-value).

scholarly journals Potential of Electricity Generation on the Western Coast of Mediterranean Sea in Egypt

2008 ◽  
Vol 45 (5) ◽  
pp. 26-38
Author(s):  
A. Ahmed Shata ◽  
S. Abdelaty ◽  
R. Hanitsch
Keyword(s):  
Wind Turbine ◽  
Mediterranean Sea ◽  
Power Law ◽  
Wind Turbines ◽  
Electricity Generation ◽  
Wind Farm ◽  
Electricity Production ◽  
Energy Output ◽  
Western Coast ◽  
Wind Potential

Potential of Electricity Generation on the Western Coast of Mediterranean Sea in EgyptA technical and economic assessment has been made of the electricity generation by wind turbines located at three promising potential wind sites: Sidi Barrani, Mersa Matruh and El Dabaa in the extreme northwest of Egypt along the Mediterranean Sea. These contiguous stations along the coast have an annual mean wind speed greater than 5.0 m/s at a height of 10 m. Weibull's parameters and the power law coefficient for all seasons have been estimated and used to describe the distribution and behavior of seasonal winds at these stations. The annual values of wind potential at the heights of 70-100 m above the ground level were obtained by extrapolation of the 10 m data from the results of our previous work using the power law. The three stations have a high wind power density, ranging from 340-425 to 450-555 W/m2at the heights of 70-100 m, respectively. In this paper, an analysis of the cost per kWh of electricity generated by two different systems has been made: one using a relatively large single 2 MW wind turbine and the other - 25 small wind turbines (80 kW, total 2 MW) arranged in a wind farm. The yearly energy output of each system at each site was determined, and the electricity generation costs in each case were also calculated and compared with those at using diesel oil, natural gas and photovoltaic systems furnished by the Egyptian Electricity Authority. The single 2 MW wind turbine was found to be more efficient than the wind farm. For all the three considered stations the electricity production cost was found to be less than 2 ϵ cent/kWh, which is about half the specific cost of the wind farm.

scholarly journals Interannual variability of wind climates and wind turbine annual energy production

2018 ◽  
Vol 3 (2) ◽  
pp. 651-665 ◽  
Author(s):  
Sara C. Pryor ◽  
Tristan J. Shepherd ◽  
Rebecca J. Barthelmie
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Interannual Variability ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Power Curve ◽  
Wind Speeds ◽  
Eastern Usa

Abstract. The interannual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standard deviation (σ) of 6 %, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub heights over the eastern USA indicate median gross capacity factors (computed using 10 min wind speeds close to wind turbine hub heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming σ=6 %. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with σ of 6 %, the results presented herein indicate that in over 90 % of the area in the eastern USA that currently has operating wind turbines, simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, the IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

scholarly journals Modified Design of Savonius Wind Turbine Blade for Performance Improvement

2019 ◽  
Vol 9 (1) ◽  
pp. 1432-1437
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
New Technologies ◽  
Vertical Axis ◽  
Horizontal Axis ◽  
Wind Speeds ◽  
Wind Potential ◽  
Horizontal Axis Wind Turbines ◽  
Savonius Wind Turbine ◽  
Low Performance

In the context of worldwide energetic transition, wind energy shows up as one of the most prominent renewable energy to provide an alternative for the conventional energy source. Therefore, new technologies of a wind turbine are developed, horizontal axis wind turbines have been extensively investigated and evolved. However, the development of vertical axis wind turbines is still an open and area of research, The main objective is to develop a more efficient type of wind turbines able to operate at low wind speeds to take hold maximum wind potential, The Savonius rotor goes with such conditions, however, it faces critical drawbacks, in particular, the low performance in comparison with horizontal axis wind turbines, as well, the blade in return of savonius wind turbine generates a negative torque leading to a decrement of turbine performance. The present work aims to investigate a modified model of the conventional Savonius rotors with a focus on improving the coefficient of power, transient computational fluid dynamics (CFD) simulations are carried out in an effort to perform a validation of numerical results according to experimental data, also to conduct a comparative analysis of both savonius models

scholarly journals Determining an Appropriate Parameter of Analytical Wake Models for Energy Capture and Layout Optimization on Wind Farms

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 739 ◽  
Author(s):  
Kyoungboo Yang
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Turbulence Intensity ◽  
Energy Production ◽  
Wind Farm ◽  
Wind Farms ◽  
Gaussian Models ◽  
Advantages And Disadvantages ◽  
Power Deficit ◽  
Wake Model

The wake of a wind turbine is a crucial factor that decreases the output of downstream wind turbines and causes unsteady loading. Various wake models have been developed to understand it, ranging from simple ones to elaborate models that require long calculation times. However, selecting an appropriate wake model is difficult because each model has its advantages and disadvantages as well as distinct characteristics. Furthermore, determining the parameters of a given wake model is crucial because this affects the calculation results. In this study, a method was introduced of using the turbulence intensity, which can be measured onsite, to objectively define parameters that were previously set according to the subjective judgement of a wind farm designer or general recommended values. To reflect the environmental effects around a site, the turbulence intensity in each direction of the wind farm was considered for four types of analytical wake models: the Jensen, Frandsen, Larsen, and Jensen–Gaussian models. The prediction performances of the wake models for the power deficit and energy production of the wind turbines were compared to data collected from a wind farm. The results showed that the Jensen and Jensen–Gaussian models agreed more with the power deficit distribution of the downstream wind turbines than when the same general recommended parameters were applied in all directions. When applied to energy production, the maximum difference among the wake models was approximately 3%. Every wake model clearly showed the relative wake loss tendency of each wind turbine.

scholarly journals Foundation Types for Land and Offshore Sustainable Wind Energy Turbine Towers

2020 ◽  
Vol 184 ◽  
pp. 01094
Author(s):  
C Lavanya ◽  
Nandyala Darga Kumar
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Wind Speeds ◽  
Deep Waters ◽  
Wind Turbine Towers

Wind energy is the renewable sources of energy and it is used to generate electricity. The wind farms can be constructed on land and offshore where higher wind speeds are prevailing. Most offshore wind farms employ fixed-foundation wind turbines in relatively shallow water. In deep waters floating wind turbines have gained popularity and are recent development. This paper discusses the various types of foundations which are in practice for use in wind turbine towers installed on land and offshore. The applicability of foundations based on depth of seabed and distance of wind farm from the shore are discussed. Also, discussed the improvement methods of weak or soft soils for the foundations of wind turbine towers.

scholarly journals Syria’s wind resources: Outlook for the Future

2020 ◽  
Vol 157 ◽  
pp. 06032
Author(s):  
Ahmad Al Jamil ◽  
Gennady Sidorenko
Keyword(s):  
Wind Turbines ◽  
Power Factor ◽  
Performance Indicators ◽  
Energy Cost ◽  
Energy Production ◽  
Wind Farm ◽  
Electricity Production ◽  
Economic Potential ◽  
Wind Speeds ◽  
Wind Resources

15 locations with wind speeds of more than 5 m/s were explored among 24 locations across Syria. Wind data from these locations was analyzed using the Weibull distribution, along with 15 different turbines. Three performance indicators were calculated and compared between each other: annual energy production, power factor (θ) and energy cost (z). The economic potential was calculated and the economic efficiency of wind turbines was studied on the basis of optimization of wind farm parameters that helped to find an option that provides the lowest price for electricity production on wind turbines. The study reveals that E70 71m 2300kw is the optimal turbine in all areas (from the places under consideration), both in terms of the highest efficiency and the lowest energy cost. The results show that the area of Sokhna has the largest economic potential because of the big space suitable for the establishment of wind turbines. Using the proposed wind farm scenario until 2030 is able to cover the deficit by 7.22%.

scholarly journals Evaluation of wind potential for an optimum choice of wind turbine generator on the sites of Lomé, Accra, and Cotonou located in the gulf of Guinea

2016 ◽  
Vol 5 (3) ◽  
pp. 211-223 ◽  
Author(s):  
Akim Adekunlé Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Mawugno Koffi Kodjo ◽  
Koffi-Sa Bedja
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farms ◽  
Gulf Of Guinea ◽  
Energy Potential ◽  
Turbine Generator ◽  
Average Speed ◽  
Wind Turbine Generator ◽  
Optimum Choice ◽  
Wind Potential

This work presents the characterization and assessment of wind energy potential in annual and monthly levels of the sites of Lomé, Accra and Cotonou located in the Gulf of Guinea, and the optimal characteristics of wind turbines to be installed on these sites. Studies of characterization and the wind potential of these sites from the wind speed data collected over a period of thirteen years at a height of 10 meters above the ground, show an annual average speed of 3.52 m/s for Lomé, 3.99 m/s for Cotonou and 4.16 m/s for Accra. These studies also showed that a monthly average speed exceeding 4 m/s was observed on the sites of Cotonou and Accra during the months of February, March, April, July, August and September and during the months of July, August and September on the site of Lomé. After a series of simulation conducted using the software named PotEol that we have developed in Scilab, we have retained that the wind turbines rated speeds of ~8 to 9 m/s at the sites of Lomé and Cotonou and ~ 9 to 10 m/s on the site of Accra would be the most appropriate speeds for optimal exploitation of electric energy from wind farms at a height of 50 m above the ground.Article History: Received May 26th 2016; Received in revised form August 24th 2016; Accepted August 30th 2016; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A , Kodjo, M.K. and Bédja, K. (2016) Evaluation of Wind Potential for an Optimum Choice of Wind Turbine Generator on the Sites of Lomé, Accra, and Cotonou Located in the Gulf of Guinea. Int. Journal of Renewable Energy Development, 5(3), 211-223.http://dx.doi.org/10.14710/ijred.5.3.211-223

Far-Field Noise Prediction of Wind Turbines at Different Receivers and Wind Speeds: A Computational Study

2015 ◽  
Author(s):  
Fardin Khalili ◽  
Pradip Majumdar ◽  
Mehdi Zeyghami
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Turbine Blades ◽  
Wind Farms ◽  
Acoustic Power ◽  
Far Field ◽  
Wind Turbine Blades ◽  
Wind Speeds ◽  
Field Noise ◽  
Field Sound

Far-field noise propagation from wind turbines propel development of wind farms to an issue for public acceptance. Airstream contains pressure fluctuations as a result of instability, giving a regular eddy pattern or an irregular turbulent motion which are responsible for the sound produced by wind turbine blades. Aeroacoustic noise emanated from a wind turbine is mainly generated by the interactions of tip and trailing edge of wind turbine blades with the mechanics in wake region such as inflow turbulence structures, boundary layer separation and vortex shedding. Hence, there is a strong necessity for an analytical investigation for noise reducing design and development of the technology in order to further expand wind farms. The objectives of this study are to analyze the far-field aeroacoustics of wind turbines with the purpose of predicting far-field sound pressure levels at different receivers and monitoring total acoustic power captured within wind turbine performance for various wind speeds. Blades are modeled based on NREL S825 airfoil since it has high maximum lift and low profile drag. With the purpose of predicting far-field noise, the Ffowcs Williams-Hawkings (FW-H) acoustics model is the preferred method in order to compute the far-field sound signal which is released from near-field flow. As the key attribute of the research, detached eddy simulation (DES) provides accurate results for the desired simulation since it is a hybrid modeling approach that combines features of Reynolds-averaged Navier-Stokes (RANS) simulation in boundary layers and irrotational flow regions, and large-eddy simulation (LES) in unsteady separation regions. In addition, SST K-Omega detached eddy turbulence model is used due to its good compromise between robustness, computational cost and accuracy. Aerodynamic and aeroacoustic analysis of a wind turbine is performed using a three-dimensional model and a commercial CFD Software, STAR-CCM+. In order to predict far-field sound pressure levels and acoustic powers on different locations, five point receivers are defined downstream of the wind turbine model. Receivers are placed one diameter, D, over the wind turbine rotor blades with 1D, 2D, 5D, 10D and 15D away from the wind turbine that represent receivers 1 to 5. Higher acoustic powers are delivered at closer receivers. It means that acoustic power fades out with larger distances. It is observed that there is a fractional variation of 61%, 17%, 6% and 3% as compared to the receiver 1 for receivers 2, 3, 4 and 5 respectively. Moreover, the results show that variation in total acoustic power is non-linear and higher acoustic powers will be captured for higher velocities. This comparison is done between wind speeds of 10m/s and 15m/s.

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