Screening of calculation methods for wind shear exponent

Abstract: In order to support the development and construction of wind farms, this paper analyzed and studied an important parameter-the wind shear exponent. Due to the influence of ground roughness, the wind shear exponents of different areas are different; in addition, because of the thermodynamic factor, the wind shear exponents are different even in the same area at different times. Therefore, to obtain an accurate value of the wind shear exponent in a certain area at a certain time, only the local wind-speed data can be used to calculate it. However, because of the complexity of the measured data, there are many methods to calculate the wind shear exponent, and the values calculated by different methods are different. So, in this paper, the methods of calculating wind shear exponent were studied. Firstly, there were five methods to calculate the wind shear exponent using different data sets, including 1) all of the data, 2) the data without wind speeds less than 3 m/s, 3) the data with the annual average wind speed, 4) the data with wind speeds between (15±0.5) m/s, 5) the wind profile. Among them, methods 1, 2, and 4 calculated wind shear exponent through the least-squares fitting. Method 3 used the annual average wind speed and the exponential formula to calculate wind shear exponent. Method 5 used wind profile fitting to calculate the wind shear exponent. The wind profile reflects the overall level of the wind conditions. Then, with the example of actual wind speed data, and within a complete year on three wind measurement heights at a mast of Wulanchabu in Inner Mongolia, five different wind shear exponents of this area were calculated by the above methods. Finally, according to the calculated wind shear exponents and the power-law formula, the wind speeds of the known height were calculated, and then by comparing the calculated value and actual value, the methods that produce smaller errors were chosen, and at last the more accurate wind shear exponent was obtained. The results showed that due to the impact of ground roughness and the topography, not only wind shear exponents were different in different areas, but they were also different when calculating by different methods even in the same area at the same time. The result of the method which used the data without wind speeds less than 3 m/s (method 2) for the least-squares fitting was more accurate than the result of the method which uses all of the data (method 1) for the least-squares fitting. The result calculated by the annual average wind speed (method 3) was close to the result calculated by using all of the data for the least-squares fitting. In the mountainous area, if it gets a negative wind shear exponent when calculated by the data with wind speeds between (15±0.5) m/s (method 4), the result will not be stable or reliable. Overall, the method of using the data without wind speeds less than 3 m/s calculated by the least-squares fitting and the method using wind profile fitting are more accurate than the other methods. Therefore, combining with the actual situation of wind farm, using these methods comprehensively to choose the smallest error wind shear exponent will provide the evaluation work with a more accurate foundation and ultimately achieve the goal of better utilization of wind resources.