#WIND SPEED PDF#
At the same time, the accurate estimated pdf can also help us to select an optimal wind energy conversion system and evaluate the reliability of generation system. If the frequency distribution of wind speed is comprehensively expressed by an estimated pdf, the wind power density and wind energy output of wind turbines can be evaluated, which can help us make a reasonable decision whether to build a wind farm in the observed area or not, and reduce the uncertainties and the errors of wind power output estimation 7. Therefore, the pdf of wind speed becomes an important basis for evaluating wind energy potential and wind stochastic characteristics 6. In this case, we can take wind speed as a random variable and describe it by a probability density function (pdf). But wind speed is not constant, it always fluctuates with the varying of air temperature over a period of time in different geographic locations and seasons. Before developing wind power in a certain site, including the design, arrangement and condition monitoring of wind turbine systems, it is necessary to assess the wind energy potential and wind characteristics 5.Īs wind energy is proportional to the cube of wind speed, this means that even a small increase in wind speed results in a large increase in wind energy, therefore, the most important factor affecting wind energy is wind speed. Wind energy also plays an important role in national economic growth which creates more employment opportunities 3, 4. Therefore, the application of wind energy has already been selected as an important measure for the sustainable development of resources and environment all over the world. Wind energy has attracted more and more attention because of its advantages such as abundance, renewability, natural cleanness, low cost and little negative impact on the environment, and has been used as an alternative to fossil fuels 1, 2.
The results show that the proposed method is effective and the area under study is not suitable for wide wind turbine applications, and the estimated wind energy potential would be inaccuracy without considering the influence of wind direction.Įnergy consumption increases dramatically with the rapid development of society and economy. The proposed method is applied to averaged 10-min field monitoring wind data and compared with the other estimation methods and judged by the values of R 2 and root mean squared error, histogram plot and wind rose diagram. A comprehensive technique of model selection, which includes Akaike information criterion, Bayesian information criterion, the coefficient of determination R 2 and root mean squared error, is used to select the optimal model in all candidate models. For modeling the distribution of wind power density and estimating model parameters of null or low wind speed and multimodal wind speed data, based on expectation–maximization algorithm, a two-component three-parameter Weibull mixture distribution is chosen as wind speed model, and a von Mises mixture distribution with nine components and six components are selected as the models of wind direction and the correlation circular variable between wind speed and direction, respectively.
Considering the correlation existing and the effect between wind speed and direction, the angular-linear modeling approach is adopted to construct the joint probability density function of wind speed and direction. Based on wind speed, direction and power data, an assessment method of wind energy potential using finite mixture statistical distributions is proposed.
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