Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument

Chaurasiya, Prem Kumar; Ahmed, Siraj; Warudkar, Vilas

doi:10.1016/j.aej.2017.08.008

Cited by 105 publications

(62 citation statements)

References 14 publications

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“…This method is also named as power density method. According to several previous works (Chaurasiya et al, 2017(Chaurasiya et al, , 2018Rocha et al, 2012;Zhang, 2015), this method uses the ratio of the cubic wind speed and the cubic of the mean speed of the wind as shown in equation 19.…”

Section: Estimation Of Weibull Parametersmentioning

confidence: 99%

Wind energy resource assessment for Suva, Fiji, with accurate Weibull parameters

Kutty

Khan

Ahmed

2019

Energy Exploration & Exploitation

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Wind resource assessment is carried out for Suva, the capital of the Republic of Fiji Islands. The wind speeds at 34 m and 20 m above ground level, wind direction, atmospheric pressure, and temperature were measured for more than five years and were statistically analyzed. The daily, monthly, yearly, and seasonal averages were estimated. For the site, the overall average wind speed at 34 m above ground level is found to be 5.18 m/s. The occurrence of effective wind (between the cut-in and cutoff wind speeds of the selected turbine) is predominantly from the east. An effective wind speed of 74.175% was recorded which can be used for power generation. The turbulence intensity and wind shear coefficient are estimated. The site's overall turbulence intensities are 12.5% and 13.72% at 34 m and 20 m above ground level, respectively. The diurnal wind shear correlated with the temperature variation very well. The overall and seasonal wind distributions are analyzed, which shows that the wind speed in Suva is mostly between 3 m/s and 9 m/s although the winter season has higher wind speeds. The Weibull parameters and the wind power density were found using 10 different methods. The wind power density is estimated to be 159 W/m 2 using the best method, which is found to be the empirical method of Justus. A highresolution map around the site is digitized and the wind power density resource map is generated using wind atlas analysis and application program. From the wind atlas analysis and application program analysis, it is seen that Suva has high potential for power generation. Five possible locations are selected for installing wind turbines and the annual energy production is estimated using wind atlas analysis and application program. The total annual energy production from the five sites is 1950 MWh. The average capacity factor of the five turbines is 17%. An economic analysis is performed which showed a payback period of 10.83 years.

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Section: Estimation Of Weibull Parametersmentioning

confidence: 99%

Wind energy resource assessment for Suva, Fiji, with accurate Weibull parameters

Kutty

Khan

Ahmed

2019

Energy Exploration & Exploitation

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“…This method constructs a linear relationship for the two variables and optimizes their relationship. The expressions below can be used to do the calculations (Ahmed, 2018;Azad et al, 2015;Chaurasiya et al, 2017):…”

Section: Least Squares Methods (Lsm)mentioning

confidence: 99%

Wind Energy Potential Assessment in Selected Regions in Northern Cyprus Based on Weibull Distribution Function

Kassem¹,

Camur²,

Abughinda³

et al. 2019

J. Eng. Appl. Sci.

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This study investigates the wind characteristics and available wind energy for six stations in Northern Cyprus, namely Famagusta, Rizokarpaso, Ercan, Nicosia, Kyrenia and Morphou. Based on 7 year (2010-2016) wind data recorded at these stations, Weibull distribution using three different methods called maximum likelihood (MLM), moment (MM) and least squares (LSM) methods were used for the statistical analysis of the obtained data at a height of 10 m. The power law model is utilised to determine the yearly and monthly mean wind speed at various heights of 30, 50, 80 and 90 m. Moreover, Wind Power Density (WPD) was estimated using three numerical methods. The results illustrated that Famagusta is the most applicable location for harvesting the kinetic energy of the wind, while Rizokarpaso has been classified as the second most applicable site. Furthermore, power density results indicated that the wind energy source in these regions is categorized to be very poor. Consequently, it is concluded that the average wind powers indicate that small-scale wind turbines are more suitable than high capacity wind turbines in the selected regions.

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“…The density of the airflow is so high that it is upward. The expression of airflow density change according to the change in height is as follow [18]:…”

Section: Extrapolation Methodsmentioning

confidence: 99%

Research on Facilities of Power Supply of Small Power Capability Consumers of Bukhara Region by using Wind and Solar Energy

2019

IJITEE

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This article analyzes the climatic features of the Bukhara regionthe southern-eastern part of Uzbekistan. The wind and solar potential of the region and the prospects of its utilization were evaluated. In recent years, wind and solar energy has been analyzed worldwide. The potential for the use of solar radiation in the region was analyzed. Accordingly, when the solar photovoltaic batteries in the region are used (coefficient of efficiency-18%), the technical potential of the region is 41 TWh/year. Wind speeds and wind energy potential data were collected over eight years (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018) method. In accordance with that, on height of 10m it makes 41.19 W/m2 and 361.48 kWh/m2, on height of 50m 117.23 W/m2 1026.96 kWh/m2, and on height of 100m 192.76 W/m2, 1688.59 kWh/m2. The wind energy gross, technical and economic potential of the region has been evaluated. In addition, the potential and prospects of using hybrid (wind and solar) power stations for the supply of uninterrupted and reliable power supply to consumers in the region were analyzed. and were calculated using the monthly wind speed data measured every 30 minutes at a height of 10m. During the estimation of wind energy potential we were used two parameters Weibull and Rayleigh distribution functions. The density power and energy values of the wind flow at various heights were evaluated using the extrapolation

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Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument

Cited by 105 publications

References 14 publications

Wind energy resource assessment for Suva, Fiji, with accurate Weibull parameters

Wind energy resource assessment for Suva, Fiji, with accurate Weibull parameters

Wind Energy Potential Assessment in Selected Regions in Northern Cyprus Based on Weibull Distribution Function

Research on Facilities of Power Supply of Small Power Capability Consumers of Bukhara Region by using Wind and Solar Energy

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