An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain)

Carrillo, C.; Cidrás, J.; Diaz-Dorado, E.; Obando-Montaño, Andrés Felipe

doi:10.3390/en7042676

Cited by 107 publications

(65 citation statements)

References 29 publications

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“…Wind data showing significant errors on these tests were removed, which turned out to be less than one percent of all measurement data tested. In addition, since the cup anemometer sensor has comparatively high wind speed thresholds, wind speed distribution estimated by cup anemometer wind speed data can cause the exaggeration of the probability of calm or low wind speed [27][28][29]. Accordingly, wind speed data below 0.5 m/s were removed from the measurements.…”

Section: Sites and Wind Datamentioning

confidence: 99%

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Kang

Huh

2018

Energies

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On Jeju Island, South Korea, an investigation was conducted to determine the best method for estimating Weibull parameters. Six methods commonly used in many fields of the wind energy industry were reviewed: the empirical, moment, graphical, energy pattern factor, maximum likelihood, and modified maximum likelihood methods. In order to improve the reliability of a research result, five-year actual wind speed data taken from nine sites with various topographical conditions were used for the estimation. Furthermore, the effect of various topographical conditions on the accuracy of the methods was analyzed and 10 bin interval types were applied to determine the most appropriate bin interval based on their performances. Weibull distributions that were estimated using these methods were compared with the observed wind speed distribution. Then the accuracy of each method was evaluated using four accuracy tests. The results showed that of the six methods, the moment method had the best performance regardless of topographical conditions, while the graphical method performed the worst. Additionally, topographical conditions did not affect the accuracy ranking of the methods for estimating the Weibull parameters, while an increase of terrain complexity resulted in an increase of discrepancy between the estimated Weibull distribution and the frequency of the observed wind speed data. In addition, the choice in bin interval greatly affected the accuracy of the graphical method while it did not depend on the accuracy of the modified maximum likelihood method.

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Section: Sites and Wind Datamentioning

confidence: 99%

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Kang

Huh

2018

Energies

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“…Histograms are generally graphed using bars to represent each bin, but in Figure 4 the values have been graphed using a normal line plot in order to compare it with the Weibull distribution of the wind data. It is clear from Figure 4 that the Weibull probability function provides a good representation of the raw wind data (Carrillo et al 2014). …”

Section: Wm10 Butterworthmentioning

confidence: 99%

Power calculation accuracy as a function of wind data resolution

Klerk

Venter

2017

J. energy South. Afr.

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“…A Weibull distribution is a putative statistical tool for analyzing wind speed data [21][22][23]. The two-parameter Weibull distribution consists of scale and shape parameters.…”

Section: Step 2: Estimate the Average Wind Turbine Output Using A Weimentioning

confidence: 99%

Stochastic Prediction of Wind Generating Resources Using the Enhanced Ensemble Model for Jeju Island’s Wind Farms in South Korea

Kim

Hur

2017

Sustainability

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Due to the intermittency of wind power generation, it is very hard to manage its system operation and planning. In order to incorporate higher wind power penetrations into power systems that maintain secure and economic power system operation, an accurate and efficient estimation of wind power outputs is needed. In this paper, we propose the stochastic prediction of wind generating resources using an enhanced ensemble model for Jeju Island's wind farms in South Korea. When selecting the potential sites of wind farms, wind speed data at points of interest are not always available. We apply the Kriging method, which is one of spatial interpolation, to estimate wind speed at potential sites. We also consider a wind profile power law to correct wind speed along the turbine height and terrain characteristics. After that, we used estimated wind speed data to calculate wind power output and select the best wind farm sites using a Weibull distribution. Probability density function (PDF) or cumulative density function (CDF) is used to estimate the probability of wind speed. The wind speed data is classified along the manufacturer's power curve data. Therefore, the probability of wind speed is also given in accordance with classified values. The average wind power output is estimated in the form of a confidence interval. The empirical data of meteorological towers from Jeju Island in Korea is used to interpolate the wind speed data spatially at potential sites. Finally, we propose the best wind farm site among the four potential wind farm sites.

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An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain)

Cited by 107 publications

References 29 publications

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Power calculation accuracy as a function of wind data resolution

Stochastic Prediction of Wind Generating Resources Using the Enhanced Ensemble Model for Jeju Island’s Wind Farms in South Korea

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