Wind Energy Explained

Manwell, James F.; McGowan, J.G.; Rogers, Anthony

doi:10.1002/0470846127

Cited by 904 publications

(308 citation statements)

References 27 publications

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“…13,14 Wind power density is directly proportional to 15 : (a) the cube of the wind speed and (b) the density of the air q. Under the assumption that air density is independent of wind speed, [15][16][17][18] wind power density has been estimated in the scientific literature. Considering the time dependence of only the wind speed, the estimation of the mean wind power density can be done by the wind power density probability density function by a univariate probability model and this assumption is been used thoroughly to wind turbine energy output estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the time dependence of only the wind speed, the estimation of the mean wind power density can be done by the wind power density probability density function by a univariate probability model and this assumption is been used thoroughly to wind turbine energy output estimation. 15,17 According to the International Standard IEC 61400-12 and other international recommendations, the two-parameter Weibull probability density function is the most appropriate distribution function for wind speed data [19][20][21] as it gives a good fit to the observed wind speed data both at surface 22 and in the upper air. 23 The mean energy output estimation for a wind turbine has been carried out by using the power curve of the wind turbine and the probability density function of wind speed at the time period considered.…”

Section: Introductionmentioning

confidence: 99%

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Wind power potential assessment for seven buoys data collection stations in Aegean Sea using Weibull distribution function

Bagiorgas

Mihalakakou

Rehman

et al. 2012

Journal of Renewable and Sustainable Energy

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This paper utilizes three hourly measured values of wind speed and direction from seven buoys data collection stations in Aegean Sea to study the wind speed and power characteristics applying the Weibull shape and scale parameters. Specifically, the site dependent, annual and monthly mean patterns of mean wind speed, Weibull parameters, frequency distribution, most probable wind speed, maximum energy carrying wind speed, wind power density and wind energy density characteristics have been studied. The Weibull distribution was found to represent the wind speed distribution with more than 90% accuracy in most of the cases. Slightly decreasing trends were observed in annual mean wind speed values at Lesvos and increasing at Mykonos. The mean values of wind speed, scale parameter, most probable wind speed, maximum energy carrying wind speed, wind power and wind energy density values showed higher values during winter time and lower in summer time. Mykonos was found to be the best site from wind power harnessing point of view. Moreover, the correlation between the percentages of times the wind speed was above cut-in-speed and the measured mean wind speed for the three selected sites and the correlation between the aforementioned percentages and the scale parameter c were examined and were found linear.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wind power potential assessment for seven buoys data collection stations in Aegean Sea using Weibull distribution function

Bagiorgas

Mihalakakou

Rehman

et al. 2012

Journal of Renewable and Sustainable Energy

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show abstract

“…Para este cálculo se utilizó una rugosidad de superficie Z 0 =0,2 mm, valor recomendado para aguas calmas en mar abierto (Barthelmie et al, 1996;Manwell et al, 2002).…”

Section: Métodounclassified

Estimación del potencial eólico off-shore en las costas de Chile utilizando datos de escaterómetro y Reanalysis

Mattar

Villar-Poblete

2014

Rev. Teledetec.

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“…It is clear that the forecasted wind speed in four hours ahead is more inaccurate than that forecasted one hour ahead [41][42][43]. The power generated through wind turbines is affected by some factors such as, wind speed and direction, turbine position and size, dynamic performance of the generator as well as the wind distribution among parallel turbines where the wind power output is mainly proportional to the wind speed [44]. There are generally two approaches to forecast wind speed as follows: a) Direct transformation approach, b) Influencing factors as independent variables.…”

Section: Wind Prediction Proceduresmentioning

confidence: 99%

On the management of wind power intermittency

Rahimi

Rabiee

Aghaei

et al. 2013

Renewable and Sustainable Energy Reviews

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Nowadays, the world is encountering severe challenges in the energy generation sector. Environmental issues like climate change, global warming and Green House Gases (GHGs) and also social issues like dramatic increase in global population and increasing energy demand are the main causes of global concerns about energy resource management. In this regard, Renewable Energy Sources (RESs) are the suitable substitution to replace the conventional generating units that emit GHGs due to the use of fossil fuels. Among all RESs, wind energy seems to be promising for generating emission-free electrical energy. However, it is naturally unpredictable due to its intermittency which leads to s ome technical problems such as generation imbalance as well as optimal reserve allocation. This paper investigates the solutions to compensate wind intermittency through introducing various technolo gies such as Pumped-Hydro Storage (PH S) units, Plug-in Hybrid Electric Vehicles (PHEVs), solar energy and other electric storages like batteries. Abstract-Nowadays, the world is encountering severe challenges in the energy generation sector. Environmental issues like climate change, global warming and Green House Gases

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Wind Energy Explained

Cited by 904 publications

References 27 publications

Wind power potential assessment for seven buoys data collection stations in Aegean Sea using Weibull distribution function

Wind power potential assessment for seven buoys data collection stations in Aegean Sea using Weibull distribution function

Estimación del potencial eólico off-shore en las costas de Chile utilizando datos de escaterómetro y Reanalysis

On the management of wind power intermittency

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