An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics

Akpınar, Ebru Kavak; Akpınar, Sinan

doi:10.1016/j.enconman.2004.08.012

Cited by 287 publications

(170 citation statements)

References 22 publications

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“…Compared to other statistical functions, two-parameter Weibull probability is the most suitable, most used and highly recommended distribution function because it provides best fitting for monthly probability density distributions in the analysis of wind speed data [25], [26]. The functions of cumulative distribution and probability density are two parameters that characterize wind speed variation in the Weibull distribution.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…TEIAS [29] shows that electricity demand load duration curves change significantly from 11 a.m. to 6 p.m. with high energy demand and from 1 a.m. to 11 a.m. with low demand. In addition, Karasu [30] In the northern hemisphere, months are in most cases split as follows for each of the 4 seasons: winter: December, January and February; spring: March, April and May; summer: June, July and August; and autumn: September, October and November [25]. Therefore, seasonal average wind speeds were computed using data of the months related to each season for each location.…”

Section: Wind Characteristicsmentioning

confidence: 99%

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Güney Batı Karadeniz Kıyıları Boyunca Rüzgar Güç Potansiyelinin Değerlendirilmesi

Akpınar

2016

SAUFenBilDer

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Information about wind characteristic together with the analysis of its potential are a key step when it comes to planning and performance estimation of wind energy projects. In the current study, hence, a statistical analysis on wind energy potential and wind characteristics over the coasts of the South Western Black sea is carried out. The aim is to determine fields where wind force is strong and consistent for energy production along south western coasts of the Black Sea. For this purpose, seven meteorological stations (Kumköy, Şile, Akçakoca, Zonguldak, Amasra, Cide, İnebolu) from west to east are selected, and 10 year wind data acquired from TSMS (Turkish Meteorological Service) measured hourly at a height of 10 m were used. Annual, seasonal, monthly and diurnal average wind speed and power density variations are evaluated at all stations, after which conclusions on dominant wind directions and frequency distributions at all sites are made. Based on overall results, Amasra and İnebolu indicated the highest potential of wind power among the other sites with Amasra having about 159 W/m 2 maximum yearly average wind power density and about 1392 kWh/m 2 /year maximum yearly average energy density.Keywords: wind speeds, wind power potential, the south western Black Sea coasts Güney Batı Karadeniz kıyıları boyunca rüzgar güç potansiyelinin değerlendirilmesi ÖZ Bir bölgenin rüzgar karakteristiğinin belirlenmesi ve onun potansiyelinin farklı açılardan analizi, rüzgar enerji projelerinin planlanması ve performans tahminlerinin yapılabilmesi için gerekli olan en önemli adımdır. Bu çalışmada, Karadeniz'in güney batı kıyılarının rüzgar karakteristiğinin ve rüzgar enerji potansiyelinin detaylı istatistiksel analizi gerçekleştirilmiştir. Çalışmadaki hedef, Karadeniz'in Türkiye kıyıları boyunca enerji üretimi için rüzgar gücü yüksek olan uygun alanları belirlemektir. Bu hedef için, bahsedilen kıyı şeridinde rüzgar ölçümlerinin gerçekleştirildiği 7 meteorolojik istasyon seçilmiş ve istatistiksel analiz için Türkiye Meteoroloji Genel Müdürlüğü tarafından arşivlenmiş 10 metre yükseklikteki saatlik rüzgar verisi (hızı ve yönü) 10 yıllık süre boyunca temin edilmiştir. Bütün istasyonlarda hakim rüzgar yönleri ve frekans dağılımları belirlendikten sonra yıllık, mevsimlik, aylık ve günlük ortalama rüzgar hız ve güç yoğunluk değişimleri de irdelenmiştir. Bütün sonuçlara dayanarak, Amasra ve İnebolu lokasyonlarının diğer alanlara kıyasla en yüksek rüzgar güç potansiyeline sahip oldukları, Amasra'nın yaklaşık 159 W/m 2 maksimum yıllık ortalama rüzgar güç yoğunluğuna ve yaklaşık 1392 kWh/m 2

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Wind Characteristicsmentioning

confidence: 99%

Güney Batı Karadeniz Kıyıları Boyunca Rüzgar Güç Potansiyelinin Değerlendirilmesi

Akpınar

2016

SAUFenBilDer

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“…A wind energy conversion system can operate at its maximum efficiency only if it is designed for a particular site because the rated power and cut-in and cut-out wind speeds must be defined based on the site wind potentialities [16]. It is essential that these parameters are selected so that energy output from the wind energy conversion system is maximized.…”

Section: Wind Turbine Energy Output and Capacity Factormentioning

confidence: 99%

“…The performance of a wind turbine installed in a given site can be examined by the amount of mean power output over a period. The capacity factor CF is defined as the ratio of the mean power output to the rated electrical power (Pr) of the wind turbine [16,17,18,19,20]. The mean energy output E and capacity factor CF of a wind turbine can be estimated using the following expressions based on Weibull distribution function [16,17,18,19]:…”

Section: Wind Turbine Energy Output and Capacity Factormentioning

confidence: 99%

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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“…data obtained over long periods is a requisite for the assessment of generation of specific plants and the relative technical-economic considerations [7]. Such data is only scarcely available in the literature [8,9].…”

Section: Introductionmentioning

confidence: 99%

Impact of Atmospheric Parameters on Power Generation of Wind Turbine

Sholapurkar¹,

Mahajan²

2016

AJEE

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Economic growth of any country depends on access to reliable energy. Wind energy is fast gaining importance among non-conventional sources, which is a function of parameters like topography of the terrain, weather conditions etc. The present work explores the potential of a 225 MW turbine located in a mountainous site in Maharashtra, India. Values of wind velocity, air temperature, density and power generation were recorded for one complete year. Analysis was done using power curves. The results show that the energy output of wind turbine is based on power curves of a specific site. The conceptual features such as energy per rated power, efficiency of wind turbine and average energy per hour are calculated. It is useful for the investor to access the wind turbine pay-back period and adopt of new optimizing technique.

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An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics

Cited by 287 publications

References 22 publications

Güney Batı Karadeniz Kıyıları Boyunca Rüzgar Güç Potansiyelinin Değerlendirilmesi

Güney Batı Karadeniz Kıyıları Boyunca Rüzgar Güç Potansiyelinin Değerlendirilmesi

Optimal selection of Wind Turbine Generators

Impact of Atmospheric Parameters on Power Generation of Wind Turbine

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