Investigation of wind power density distribution using Rayleigh probability density function

Lizica-Simona, Paraschiv; Paraschiv, Spiru; Ion, Ion V.

doi:10.1016/j.egypro.2018.11.320

Cited by 21 publications

(14 citation statements)

References 5 publications

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“…Ground obstructions retard the movement of air close to the ground surface, causing a reduction of wind speed. Several methods are used to correct the wind speed for different height [18][19][20][21][22][23][24]. Logarithm law, which is used in many research work in the literature [18,19], is an empirical formula in which the mean wind speed at a given height using this law can be evaluated as follows:…”

Section: Corrected Wind Speed Data At Different Heightmentioning

confidence: 99%

Comparative study between measured and estimated wind energy yield

Al-Quraan¹,

Al-Mahmodi²,

Radaideh³

et al. 2020

Turk J Elec Eng & Comp Sci

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This paper proposes a power-speed (P-V) model of the wind turbine by assuming three different functions for the first performance region; cubic, quadratic and uncorrected cubic. These three functions have been compared with the manufacturer models of five different wind turbines which were installed in five different locations in Jordan; Tafila, Hofa, Fujeij, Al Rajef, and Deahan. The wind turbine of these wind farms are considered as large scale HAWT in the range of Mw. The generated P-V models are developed by applying a new method described in this paper which is basically based on generating a multiplier factor x. In this study, the quadratic model shows the highest correlation compared with the other models. The wind energy yield for the selected wind farms has been estimated by a mathematical modelling based on Rayleigh distribution function, derived in this paper. The energy yield using this mathematical model has been compared with the measured energy output of four wind farms, Tafila, Hofa, Al Rajef, and Deahan. The measured energy were provided by the operators of these wind farms which are: Jordan Wind Project Company(JWPC), Central Electricity Generation Company (CEGC), Green Watts Renewable Energy (GWRC)and Korean Southern Power Company(KOSPO). Results show that the estimated energy using the quadratic wind turbine model for all wind farms are very close to the actual output. Accuracy analysis for the quadratic model resulted in an error of less than 10 % between the measured and estimated energy output for all wind farms. The capacity factors for the selected wind farms have been estimated using the quadratic P-V model. Results show that Tafila wind farm has the highest capacity factor which is around 47 % .

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Section: Corrected Wind Speed Data At Different Heightmentioning

confidence: 99%

Comparative study between measured and estimated wind energy yield

Al-Quraan¹,

Al-Mahmodi²,

Radaideh³

et al. 2020

Turk J Elec Eng & Comp Sci

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“…[2][3][4][5][6][7] Other distributions largely employed worldwide are the lognormal, truncated normal, gamma, inverse normal, beta, and generalized gamma distributions. [2][3][4][5][6][7] Other distributions largely employed worldwide are the lognormal, truncated normal, gamma, inverse normal, beta, and generalized gamma distributions.…”

Section: Single and Mixture Distributionsmentioning

confidence: 99%

“…Among the mathematical models originally used, the one-parameter Rayleigh distribution and the twoparameter Weibull distributions have become the most widespread and accepted probability distributions given their simplicity. [2][3][4][5][6][7] Other distributions largely employed worldwide are the lognormal, truncated normal, gamma, inverse normal, beta, and generalized gamma distributions. 8 For example, the effectiveness of the popular Weibull and Rayleigh distributions was assessed in North Dakota (USA) by considering also other distributions such as gamma, lognormal, and inverse normal derived probability density functions (PDFs).…”

Section: Single and Mixture Distributionsmentioning

confidence: 99%

A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

2019

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Summary The use of wind speed probability density functions is a standard practice to represent different wind regimes. Generally, these regimes are distinguished by the following three characteristics: the shape of the distribution in the central wind speeds, amount of the calm wind speeds (CWS), and extreme wind speeds (EWS). An in‐depth review has highlighted that none of the parametric distributions available is suitable to represent the three main characteristics at the same time. To overcome this gap, the use of the corrected mixture of two truncated normal distributions (CMTTND) and corrected single truncated normal distribution (CTND) are proposed to represent, respectively, bimodal and unimodal wind speed distribution shapes. The CMTTND and CTND are obtained by introducing a correction, respectively, to the mixture of two truncated normal distributions (MTTND) and to the single truncated normal distribution (TND). The MTTND and TND permit an accurate representation of distributions with high levels of CWS. The CMTTND and CTND employ a new parameter, to accurately quantifying also the relative frequencies associated with EWS. The performance of the CMTTND and CTND was assessed using a goodness‐of‐fit (GOF) test and statistical measures of error in the evaluation of the characteristic mean wind speeds. The analytical expressions of these mean wind speeds are obtained and validated by a numerical integration method for the first time in this work. The accuracy of these distributions is compared with that of other conventional probability distribution models, of which three are unimodal and six bimodal, in four Italian locations and three American locations. The analysis of the results showed that the CTND and CMTTND allow obtaining high GOF of the experimental distributions with R2 and RMSE higher and lower than, respectively, 0.977 and 0.054. Moreover, the CTND results in the most accurate distribution in the estimation of the characteristic mean wind speeds in the case of localities with unimodal experimental distributions and the CMTTND in the case of localities with bimodal experimental distributions. Contrary to other distribution, CTND and CMTTND accuracies grow by increasing the grade of the characteristic mean wind speed by reaching also estimation values lower than 2% of the real ones. This is a great advantage in the wind energy source determination in a location since the available energy depends on the mean cubic wind speed.

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“…The PDF describes the fraction of time, at a given site, that the wind speed falls in each speed range (e.g., 12–13 ms −1 ). It is usually represented by the two‐parameter Weibull distribution 10–19 . Combined with the turbine power curve, the PDF can be used to estimate the important capacity factor for the installed turbine using the “method of bins” (see Chang et al 20 …”

Section: Introductionmentioning

confidence: 99%

Global patterns of offshore wind variability

West

Smith

2021

Wind Energy

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Using the 40‐year hourly gridded ERA5 reanalysis, we study the offshore patterns of wind variability using the probability density function (PDF) and the power spectral density (PSD). To summarize wind variability, we compute the Weibull parameters from the PDF and the PSD for six spectral bands: interannual, annual, multimonth, storm, diurnal, and semidiurnal. We characterize the storm spectral peak using a Gaussian function in normallog10 frequency space. These parameters are plotted along two pole‐to‐pole transects through the Atlantic Ocean at 25°W and the Pacific Ocean at 170°W longitude and at 16 current and proposed offshore wind sites. We test the idea that coastal and open‐ocean wind statistics may be described by a single set of meridional profiles. In mid‐latitudes, the storm band variance exceeding 20 m2 s−2 dominates. The Weibull shape parameter is close to k=2. The storm interval varies from 4 to 7 days depending on location. In the Northern Hemisphere only, the annual variance is significant, exceeding 3 m2 s−2. In the equatorial region, we find steadier winds with small storm variance less than 3 m2 s−2. The Weibull shape parameter is k=4 or greater, suggesting the possibility of a high capacity factor there. The storm interval can exceed 15 days. The equatorial zone has an annual wind variance reaching 2 m2 s−2, comparable to the storm variance. In the tropical Pacific, we find interannual variance associated with El Niño‐Southern Oscillation (ENSO). Semidiurnal variance is detected in the tropics from the atmospheric tide.

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Investigation of wind power density distribution using Rayleigh probability density function

Cited by 21 publications

References 5 publications

Comparative study between measured and estimated wind energy yield

Comparative study between measured and estimated wind energy yield

A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

Global patterns of offshore wind variability

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