Abstract
This paper studied the joint probability distribution of wind speed, wind direction, and wind height. The measured wind field data of a coastal plain in Zhongshan city, Guangdong Province, China, were taken as the research object. A three-dimensional joint distribution modeling method, based on the copula function and the AL (angular–linear) model, is proposed. Firstly, the wind speed is modeled by the common distribution model, and the Weibull distribution is selected. Secondly, the mvM (mixed von Mises distribution) was used to fit the wind direction probability density, and the joint distribution of wind speed and wind direction was established based on the AL model. Finally, a three-dimensional joint distribution model of wind speed, wind direction, and height was established by considering the effect of height through the copula function. The results showed that Weibull distribution can better describe the wind speed distribution in this region. The north–south wind prevailed in this region, and the probability of the main wind direction decreased with the increase in height. The joint distribution of wind speed and direction, based on the AL model, fitted well with the measured data, and the final three-dimensional distribution model had a good fitting effect.
Highlights
As a common natural phenomenon, wind profoundly affects our lives
The results show that the joint probability density function of wind speed and direction derived from the AL model is better than that based on the multiplication theorem, and that ignoring the effects of wind direction significantly improves estimates of extreme wind speeds
The formula for connecting two-dimensional distribution function with the copula function is derived and a modeling method is given for the three-dimensional joint distribution of wind speed, wind direction, and height
Summary
As a common natural phenomenon, wind profoundly affects our lives. On the one hand, with the development of society and the global economy, humankind’s demand for energy is increasing, and wind energy has been widely used as a clean energy in recent years [1,2]. The volume of human buildings is gradually increasing, and more and more skyscrapers and large-span bridges have appeared, which are sensitive to wind [3]. Whether it is for wind energy evaluation or structural wind resistance design, the study of wind characteristics is of fundamental importance. For huge measured data, using the appropriate distribution model of wind speed and direction can and effectively describe its law. Jianzhou Wang et al [6] took four locations in central China as examples to compare commonly used wind speed probability distribution models and estimation methods of corresponding parameters. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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