Abstract
The atmospheric stability and ground topography play an important role in shaping wind-speed profiles. However, the commonly used power-law wind-speed extrapolation method is usually applied, ignoring atmospheric stability effects. In the present work, a new power-law wind-speed extrapolation method based on atmospheric stability classification is proposed and evaluated for flows over different types of terrain. The method uses the wind shear exponent estimated in different stability conditions rather than its average value in all stability conditions. Four stability classification methods, namely the Richardson Gradient (RG) method, the Wind Direction Standard Deviation (WDSD) method, the Wind Speed Ratio (WSR) method and the Monin–Obukhov (MO) method are applied in the wind speed extrapolation method for three different types of terrain. Tapplicability is analyzed by comparing the errors between the measured data and the calculated results at the hub height. It is indicated that the WSR classification method is effective for all the terrains investigated while the WDSD method is more suitable in plain areas. Moreover, the RG and MO methods perform better in complex terrains than the other methods, if two-level temperature data are available.
Highlights
In the feasibility study and microsite selection stage of wind farms, using the wind measurement data is a key step to evaluate the wind resources at the hub height
The power law (PL) method is widely used for estimating the wind speed at a wind generator hub height [22], which is defined as u2 = u1 (z2 /z1 )α where u1 is the wind speed at the height z1, u2 is the wind speed at the height z2, and α is the wind shear exponent
The results indicate the difference of the wind speed distribution between the plain and mountain areas
Summary
In the feasibility study and microsite selection stage of wind farms, using the wind measurement data is a key step to evaluate the wind resources at the hub height. Wharton and Lundquist [17] analyzed the wind farm operating data and found that, when the atmosphere was stable and the wind speed was in the range of 5~8.5 m/s, the output power was obviously greater than that in unstable conditions with strong convection, and the average difference was close to 15%. Gualtieri and Secci [21] tested the power law extrapolation model over a flat rough terrain in the Apulia region (Southern Italy), and investigated the effect of atmospheric stability and surface roughness on wind speed. They found that the empirical JM Weibull distribution extrapolating model was proved to be preferable. The model is validated against the measured data, and the suitability of different atmospheric stability classification methods for flows over different types of terrain is indicated
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