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Abstract: The fluctuation of wind speed within a specific time period affects a lot the energy conversion rate of wind turbine. In this paper, the concept of fractal dimension in chaos theory is applied to investigate wind speed characterizations; numerical algorithms for the calculation of the fractal dimension are presented graphically. Wind data selected is observed at three wind farms experiencing different climatic conditions from 2006 to 2008 in Taiwan, where wind speed distribution can be properly classified to high wind season from October to March and low wind season from April to September. The variations of fractal dimensions among different wind farms are analyzed from the viewpoint of climatic conditions. The results show that the wind speeds studied are characterized by medium to high values of fractal dimension; the annual dimension values lie between 1.61 and 1.66. Because of monsoon factor, the fluctuation of wind speed during high wind months is not as significant as that during low wind months; the value of fractal dimension reveals negative correlation with that of mean wind speed, irrespective of wind farm considered. For a location where the wind distribution is well described by Weibull function, its fractal dimension is not necessarily lower. These findings are useful to wind analysis. [Copyright &y& Elsevier]

The capacity factor is an essential indicator in evaluating a wind turbine's efficiency. In this paper, four kinds of power curve models--linear, quadratic, cubic, and general--are applied to estimate the capacity factor of a pitch-controlled wind turbine based on the Weibull probability distribution of wind speed. The general model is adopted for the first time in this issue. The wire-frame graph of capacity factor is demonstrated for practical Weibull shape and scale parameters representing various wind farms. To analyze the validity of the four empirical models, seven power output curves provided by different manufacturers are selected for different operating speeds. The results show that a turbine generator installed at a wind site with larger scale and shape parameters may show greater performance, but limitations do exist. The capacity factors calculated from manufacturer data are far greater than those from empirical models if the cut-in, rated, and furling speeds of a wind turbine are set as 4, 15, and 25 m/s, respectively. Similar values from empirical models get closer to those from manufacturer data when the cut-in or rated speeds decrease. The quadratic model shows better agreement with manufacturers' power curves. [ABSTRACT FROM AUTHOR]