Wind Energy Potential of Coastal Eritrea: An Analysis of Sparse Wind Data

Rosen, Karen; Buskirk, Robert Van; Garbesi, Karina

doi:10.1016/s0038-092x(99)00026-2

Cited by 36 publications

(19 citation statements)

References 2 publications

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“…In this paper, the long-term AWS is used to represent a wind resource, which has the following two advantages: (i) a one-parameter distribution of wind speed can be readily derived from the AWS to represent the approximate variation of wind conditions at the concerned site; and (ii) the resource strength of the site can be readily related to a wind map that represents the wind resources over a region in terms of their estimated AWS. In the context of the first advantage (stated above), it is important to note that both the Weibull and Rayleigh distribution are known to provide an acceptable description of wind speed probability [22][23][24][25] (although the former is more popular and, in general, more accurate). The Rayleigh distribution is a one-parameter model, and we exploit this characteristic to derive an approximate wind speed distribution from the "average wind speed" information; where a unique value of the distribution parameter can be determined from a given value of the mean of the distribution.…”

Section: Geographical Variation Of Wind Patternsmentioning

confidence: 99%

Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes

et al. 2016

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Abstract:The suitability of turbine configurations to different wind resources has been traditionally restricted to considering turbines operating as standalone entities. In this paper, a framework is thus developed to investigate turbine suitability in terms of the minimum cost of energy offered when operating as a group of optimally-micro-sited turbines. The four major steps include: (i) characterizing the geographical variation of wind regimes in the onshore U.S. market; (ii) determining the best performing turbines for different wind regimes through wind farm layout optimization; (iii) developing a metric to quantify the expected market suitability of available turbine configurations; and (iv) exploring the best tradeoffs between the cost and capacity factor yielded by these turbines. One hundred thirty one types of commercial turbines offered by major global manufacturers in 2012 are considered for selection. It is found that, in general, higher rated power turbines with medium tower heights are the most favored. Interestingly, further analysis showed that "rotor diameter/hub height" ratios greater than 1.1 are the least attractive for any of the wind classes. It is also observed that although the "cost-capacity factor" tradeoff curve expectedly shifted towards higher capacity factors with increasing wind class, the trend of the tradeoff curve remained practically similar.

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Section: Geographical Variation Of Wind Patternsmentioning

confidence: 99%

Market Suitability and Performance Tradeoffs Offered by Commercial Wind Turbines across Differing Wind Regimes

et al. 2016

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“…It has been concluded by Garcia et al that the two-parameter Weibull probability model fits the real wind data better than the lognormal, gamma and Rayleigh models [3,5,6]. In other words, most wind speed distribution characteristics at any site can be described by two parameters: the shape parameter k, and the scale parameter C. The fraction of time duration that the wind blows at speed V is thus determined by:…”

Section: Theoriesmentioning

confidence: 99%

“…Selection of the right machine for the right site plays a capital role in the success of the project. Depending on the Weibull scale and shape parameters, it is possible to identify the rated speed suitable for a particular wind regime [4,5,13]. Selecting the appropriate machine to a given site leads to …”

Section: Figs 4: Comparison Of Power Curves For Different Rated Wind mentioning

confidence: 99%

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Optimal selection of Wind Turbine Generators

Bencherif¹,

Brahmi²,

Chikhaoui³

2014

IJCA

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This paper examines optimum selection of wind turbines between site and wind turbine generators. An analysis methodology is done at the planning and development stages of installation of wind power stations will enable the wind power developer or the power utilities to make a judicious and rapid choice of suitable wind energy conversion system from the available potential sites. The methodology of analysis is based on the computations of annual capacity factors, which are done using the Weibull distribution function and power curve model. The methodology helps to the determination of the speeds characteristic range of the wind machines and to make easy the choice of the suitable wind turbine for a given site, in order to maximize the delivered energy for a given amount of available wind energy. This methodology is applied to install a wind energy conversion system at four sites in Algeria.

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“…Our purpose is to obtain a compact description of these variables in a vertical range in the low atmosphere, not well researched to date. The use of the lognormal distribution in wind speed analysis is infrequent (Bogardi and Matyasovszky, 1996;Garcı´a et al, 1998), the most widely used distribution for fitting wind speed observations in time and space being the Weibull distribution (Ramachandra et al, 1997;Rosen et al, 1999;Torres et al, 1999;Jaramillo et al, 2004b;Vogiatzis et al, 2004). A simpler version, the Rayleigh distribution, is also occasionally considered (Ulgen and Hepbasli, 2002;Kose et al, 2004;Akpinar and Akpinar, 2005).…”

Section: Introductionmentioning

confidence: 99%