The probability distributions of wind speeds and the availability of wind turbines were investigated by considering the vertical wind shear. Based on the wind speed data at the standard height observed at a wind farm,...The probability distributions of wind speeds and the availability of wind turbines were investigated by considering the vertical wind shear. Based on the wind speed data at the standard height observed at a wind farm, the power-law process was used to simulate the wind speeds at a hub height of 60 m. The Weibull and Rayleigh distributions were chosen to express the wind speeds at two different heights. The parameters in the model were estimated via the least square(LS) method and the maximum likelihood estimation(MLE) method, respectively. An adjusted MLE approach was also presented for parameter estimation. The main indices of wind energy characteristics were calculated based on observational wind speed data. A case study based on the data of Hexi area, Gansu Province of China was given. The results show that MLE method generally outperforms LS method for parameter estimation, and Weibull distribution is more appropriate to describe the wind speed at the hub height.展开更多
Assessing wind energy is a key step in selecting a site for a wind farm. The accuracy of the assessment is essential for the future operation of the wind farm. There are two main methods for assessing wind power: one ...Assessing wind energy is a key step in selecting a site for a wind farm. The accuracy of the assessment is essential for the future operation of the wind farm. There are two main methods for assessing wind power: one is based on observational data and the other relies on mesoscale numerical weather prediction(NWP). In this study, the wind power of the Liaoning coastal wind farm was evaluated using observations from an anemometer tower and simulations by the Weather Research and Forecasting(WRF) model, to see whether the WRF model can produce a valid assessment of the wind power and whether the downscaling process can provide a better evaluation. The paper presents long-term wind data analysis in terms of annual, seasonal, and diurnal variations at the wind farm, which is located on the east coast of Liaoning Province. The results showed that, in spring and summer, the wind speed, wind direction, wind power density, and other main indicators were consistent between the two methods. However, the values of these parameters from the WRF model were significantly higher than the observations from the anemometer tower. Therefore, the causes of the differences between the two methods were further analyzed. There was much more deviation in the original material, National Centers for Environmental Prediction(NCEP) final(FNL) Operational Global Analysis data, in autumn and winter than in spring and summer. As the region is vulnerable to cold-air outbreaks and windy weather in autumn and winter, and the model usually forecasted stronger high or low systems with a longer duration, the predicted wind speed from the WRF model was too large.展开更多
基金Project(51165019)supported by the National Natural Science Foundation of ChinaProject(1308RJYA018)supported by Gansu Provincial Natural Science Fund,ChinaProject(2013-4-110)supported by Lanzhou Technology Development Program,China
文摘The probability distributions of wind speeds and the availability of wind turbines were investigated by considering the vertical wind shear. Based on the wind speed data at the standard height observed at a wind farm, the power-law process was used to simulate the wind speeds at a hub height of 60 m. The Weibull and Rayleigh distributions were chosen to express the wind speeds at two different heights. The parameters in the model were estimated via the least square(LS) method and the maximum likelihood estimation(MLE) method, respectively. An adjusted MLE approach was also presented for parameter estimation. The main indices of wind energy characteristics were calculated based on observational wind speed data. A case study based on the data of Hexi area, Gansu Province of China was given. The results show that MLE method generally outperforms LS method for parameter estimation, and Weibull distribution is more appropriate to describe the wind speed at the hub height.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA05110305)
文摘Assessing wind energy is a key step in selecting a site for a wind farm. The accuracy of the assessment is essential for the future operation of the wind farm. There are two main methods for assessing wind power: one is based on observational data and the other relies on mesoscale numerical weather prediction(NWP). In this study, the wind power of the Liaoning coastal wind farm was evaluated using observations from an anemometer tower and simulations by the Weather Research and Forecasting(WRF) model, to see whether the WRF model can produce a valid assessment of the wind power and whether the downscaling process can provide a better evaluation. The paper presents long-term wind data analysis in terms of annual, seasonal, and diurnal variations at the wind farm, which is located on the east coast of Liaoning Province. The results showed that, in spring and summer, the wind speed, wind direction, wind power density, and other main indicators were consistent between the two methods. However, the values of these parameters from the WRF model were significantly higher than the observations from the anemometer tower. Therefore, the causes of the differences between the two methods were further analyzed. There was much more deviation in the original material, National Centers for Environmental Prediction(NCEP) final(FNL) Operational Global Analysis data, in autumn and winter than in spring and summer. As the region is vulnerable to cold-air outbreaks and windy weather in autumn and winter, and the model usually forecasted stronger high or low systems with a longer duration, the predicted wind speed from the WRF model was too large.
