Comparative analysis of Weibull parameters for wind data measured from met-mast and remote sensing techniques

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

doi:10.1016/j.renene.2017.08.014

Cited by 65 publications

(38 citation statements)

References 24 publications

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“…In this method, the cumulative distribution function of the Weibull distribution is transformed into a linear regression, and the shape factor (k) and the scale factor (A) is computed using the equations (16) and (17) (Chaurasiya et al 2018)…”

Section: Least Squares Fit Methodsmentioning

confidence: 99%

“…This method was also used by Herrero-Novoa et al (2017) for studying the wind characteristics and power density in northern Spain. Nine different methods of estimating Weibull parameters were employed by Chaurasiya et al (2018) for the wind data at Kayathar in India. They found that, at 80 m, the best method to evaluate k and A is the MML method, while at 100 m height, the ML method gave more suitable results.…”

Section: Introductionmentioning

confidence: 99%

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Wind energy resource assessment for Vanuatu with accurate estimation of Weibull parameters

Singh

Bule

Khan

et al. 2019

Energy Exploration & Exploitation

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Wind energy resource assessments for Pentecost Island and Epi Island in Vanuatu were carried out using one year of wind data. The wind data were used to calculate the daily average wind speed, diurnal variation of wind speed and monthly average wind speed. The diurnal variation of wind shear coefficient of the site was also studied and it correlated well with the temperature variation. Ten methods were used to determine the Weibull parameters and the wind power density of the site. The best method was determined using the goodness of fit test/error where the correlation coefficient, coefficient of efficiency, root mean square error, maximum absolute error and maximum absolute percentage error for the 10 methods were compared. It was found that the moments method was the best method for obtaining the shape parameter (k), the scale parameter (A) and the correct wind power density for the Pentecost Island site, whereas for the Epi site, the median and quartiles method performed the best. The mean wind speed for the Pentecost site was 5.60 m/s, while that for the Epi site was 5.86 m/s. The Weibull parameters were also estimated for the two seasons for both the islands. The wind resource maps showing the wind power density were also obtained. The annual energy production from 10 Vergnet 275 kW wind turbines, positioned at good locations on the digital wind map, was estimated. Finally, an economic analysis of the turbines was carried out, which indicated a payback period of 4.85 years.

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Section: Least Squares Fit Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wind energy resource assessment for Vanuatu with accurate estimation of Weibull parameters

Singh

Bule

Khan

et al. 2019

Energy Exploration & Exploitation

View full text Add to dashboard Cite

show abstract

“…Due to its extremely high flexibility, the Weibull distribution [1] is widely used for fitting engineering data, such as the strength of materials [2,3], fracture of brittle materials [4,5], and wind speed [6,7]. The three-parameter Weibull distribution is very flexible for random data fitting so that it has a strong adaptability for different types of probability distribution.…”

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Estimation for Three‐Parameter Weibull Distribution Using Evolutionary Strategy

Yang

Ren

2019

Mathematical Problems in Engineering

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The maximum likelihood estimation is a widely used approach to the parameter estimation. However, the conventional algorithm makes the estimation procedure of three-parameter Weibull distribution difficult. Therefore, this paper proposes an evolutionary strategy to explore the good solutions based on the maximum likelihood method. The maximizing process of likelihood function is converted to an optimization problem. The evolutionary algorithm is employed to obtain the optimal parameters for the likelihood function. Examples are presented to demonstrate the proposed method. The results show that the proposed method is suitable for the parameter estimation of the three-parameter Weibull distribution.

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“…In recent years, the need for energy is constantly increasing due to the developing technology and the increasing population in the world [1]. The fact that the fossil energy sources will be exhausted and the harmful effects on the environment cause all countries to turn to renewable energy sources [2]. The needs of countries to meet their energy deficits are seriously affecting countries politically and economically.…”

Section: Introductionmentioning

confidence: 99%

Determination of Weibull parameters with using Standard Deviation Method and performance comparison at different locations

Kaplan

2019

Scientia Iranica

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This paper analyzed the harmony of the Weibull Distribution Function (WDF) and the real data obtained from three different locations. The Standard Deviation Method (SDM) was used to calculate the coefficients of the WDF at Adana, Osmaniye and Hatay regions. One of the important purposes of this study is to observe how the performance of the SDM changes in regions with different mean wind speeds. The statistical fittings of the calculated and measured hourly wind speed data were evaluated for justifying the performance of the SDM. According to the results, Root Mean Square Error (RMSE) , Coefficient of Determination (R 2 ) and Mean Percentage Error (MPE) error analyses tests were used to evaluate the performance of mentioned method in all three selected regions. The performance of the method mentioned in this study was examined in detail at different regions with different geographic characteristics. For the selected three regions, the performance of the SDM was evaluated at different mean wind speeds varying over the years. In addition, the performance of the selected method in three different regions was examined in detail in the same years and it was observed how geographical factors affected the performance of the method.

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Comparative analysis of Weibull parameters for wind data measured from met-mast and remote sensing techniques

Cited by 65 publications

References 24 publications

Wind energy resource assessment for Vanuatu with accurate estimation of Weibull parameters

Wind energy resource assessment for Vanuatu with accurate estimation of Weibull parameters

Maximum Likelihood Estimation for Three‐Parameter Weibull Distribution Using Evolutionary Strategy

Determination of Weibull parameters with using Standard Deviation Method and performance comparison at different locations

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