A comparative analysis of wind speed probability distributions for wind power assessment of four sites

Sohoni, Vaishali; Gupta, Shivcharan; Nema, R. K.

doi:10.3906/elk-1412-207

Cited by 66 publications

(40 citation statements)

References 30 publications

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“…Pearson VI, uniform, and Weibull distribution functions[32] [33][34]. On our study site, it has been demonstrated that the Weibull distribution with two parameters (shape parameter and scale parameter) developed in 1951 by Waloddi Weibull is suitable to analyze and represent the wind rate distribution.…”

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confidence: 99%

Vertical Profile of Wind Speed in the Atmospheric Boundary Layer and Assessment of Wind Resource on the Bobo Dioulasso Site in Burkina Faso

Boro¹,

Donnou²,

Kossi³

et al. 2019

SGRE

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This study investigates both the characteristics of the vertical wind profile at the Bobo Dioulasso site located in the Sudanian climate zone in Burkina Faso during a day and night convective wind cycle and the estimation and variability of the wind resource. Wind data at 10 m above ground level and satellite data at 50 m altitude in the atmospheric boundary layer were used for the period going from January 2006 to December 2016. Based on Monin-Obukhov theory, the logarithmic law and the power law made it possible to characterize the wind profile. On the study site, the atmosphere is generally unstable from 10:00 to 18:00 and stable during the other periods of the day. Wind extrapolation models were tested on our study site. Fitting equations proposed are always in agreement with the data, contrary to other models assessed. Based on these equations, the profile of a day and night cycle wind cycle was established by extrapolation of wind data measured at 10 m above the ground. Lastly, the model of the power law based on the stability was used to generate data on wind speed from 20 m to 50 m based on data from 10 m above the ground. Weibull function was used to characterize wind speed rate distribution and to calculate wind energy potential. The average annual power density on the site is estimated at 53.13 W/m 2 at 20 m and at 84.05 W/m 2 at 50 m, or 36.78% increase. Considering these results, the Bobo-Dioulasso site could be appropriate to build small and medium-size turbines to supply the rural communities of the Bobo Dioulasso region with electricity.

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confidence: 99%

Vertical Profile of Wind Speed in the Atmospheric Boundary Layer and Assessment of Wind Resource on the Bobo Dioulasso Site in Burkina Faso

Boro¹,

Donnou²,

Kossi³

et al. 2019

SGRE

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“…(2) Parameters obtained by evolutionary techniques for SCADA data in [1] 4PL [34,35] = = maximum asymptote = asymmetry factor (parameters obtained by evolutionary techniques) curve by using the equation of a straight line. The resulting curve follows the actual curve more accurately.…”

Section: Polynomial Function Approximationmentioning

confidence: 99%

“…It is shown in [1] that this model can be used for online monitoring of power curves. Another form of four PL expression is used for extracting power curve from actual wind speed and power curve data of a wind farm in [35]. The method is applied for wind energy estimation of the selected wind farm site.…”

Section: Journal Of Energymentioning

confidence: 99%

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A Critical Review on Wind Turbine Power Curve Modelling Techniques and Their Applications in Wind Based Energy Systems

2016

Self Cite

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Power curve of a wind turbine depicts the relationship between output power and hub height wind speed and is an important characteristic of the turbine. Power curve aids in energy assessment, warranty formulations, and performance monitoring of the turbines. With the growth of wind industry, turbines are being installed in diverse climatic conditions, onshore and offshore, and in complex terrains causing significant departure of these curves from the warranted values. Accurate models of power curves can play an important role in improving the performance of wind energy based systems. This paper presents a detailed review of different approaches for modelling of the wind turbine power curve. The methodology of modelling depends upon the purpose of modelling, availability of data, and the desired accuracy. The objectives of modelling, various issues involved therein, and the standard procedure for power performance measurement with its limitations have therefore been discussed here. Modelling methods described here use data from manufacturers’ specifications and actual data from the wind farms. Classification of modelling methods, various modelling techniques available in the literature, model evaluation criteria, and application of soft computing methods for modelling are then reviewed in detail. The drawbacks of the existing methods and future scope of research are also identified.

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“…In [13], a three-parameter logistic function (3-PLF) was used by the authors to evaluate the performance of the Gamsesa G90-2.0 MV wind turbine. Many researchers suggested using a four-parameter logistic function (4-PLF) to model WTPC [14,15]. When the variable asymmetry factor is added into the above 4-PLF, a five-parametrer logistic function (5-PLF) can be derived.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique

Pei

2019

Applied Sciences

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A power curve of a wind turbine describes the nonlinear relationship between wind speed and the corresponding power output. It shows the generation performance of a wind turbine. It plays vital roles in wind power forecasting, wind energy potential estimation, wind turbine selection, and wind turbine condition monitoring. In this paper, a hybrid power curve modeling technique is proposed. First, fuzzy c-means clustering is employed to detect and remove outliers from the original wind data. Then, different extreme learning machines are trained with the processed data. The corresponding wind power forecasts can also be obtained with the trained models. Finally, support vector regression is used to take advantage of different forecasts from different models. The results show that (1) five-parameter logistic function is superior to the others among the parametric models; (2) generally, nonparametric power curve models perform better than parametric models; (3) the proposed hybrid model can generate more accurate power output estimations than the other compared models, thus resulting in better wind turbine power curves. Overall, the proposed hybrid strategy can also be applied in power curve modeling, and is an effective tool to get better wind turbine power curves, even when the collected wind data is corrupted by outliers.

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A comparative analysis of wind speed probability distributions for wind power assessment of four sites

Cited by 66 publications

References 30 publications

Vertical Profile of Wind Speed in the Atmospheric Boundary Layer and Assessment of Wind Resource on the Bobo Dioulasso Site in Burkina Faso

Vertical Profile of Wind Speed in the Atmospheric Boundary Layer and Assessment of Wind Resource on the Bobo Dioulasso Site in Burkina Faso

A Critical Review on Wind Turbine Power Curve Modelling Techniques and Their Applications in Wind Based Energy Systems

Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique

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