The wind speed is measured with the help of three anemometers S30, S45, S60 placed at 30 m, 45 m, and 60 m height. Mean values are recorded and stored for every hour using a data logger. For accounting wind turbine ge...The wind speed is measured with the help of three anemometers S30, S45, S60 placed at 30 m, 45 m, and 60 m height. Mean values are recorded and stored for every hour using a data logger. For accounting wind turbine generator （WTG.） tower height, data recorded from S60 anemometer at 60 m height is used for analysis purpose. This paper analyzes the probability distribution of wind speed data recorded by maharashtra energy development agency （MEDA） wind farm at Ahmednagar （India）. The main objective is to validate the wind energy probability by using probability distribution function （PDF） of available wind potential. The energy generated from wind for any time interval is equal to the area tinder power curve multiplied by time in hours for that time interval. To estimate the wind energy probability, hourly wind speed data tbr one year interval is selected. Weibull distribution is adopted in this study to best fit the wind speed data. The scale and shape paranleters are estimated by using maximum likelihood method. The goodness of fit tests based on the probability density function （PDF） is conducted to show that the distribution adequately fits the data. It is found from the curve fitting test that, although the two distributions are all suitable for describing the probability distribution of wind speed data, the two-parameter weibull distribution is more appropriate than the lognormal distribution.展开更多
文摘The wind speed is measured with the help of three anemometers S30, S45, S60 placed at 30 m, 45 m, and 60 m height. Mean values are recorded and stored for every hour using a data logger. For accounting wind turbine generator （WTG.） tower height, data recorded from S60 anemometer at 60 m height is used for analysis purpose. This paper analyzes the probability distribution of wind speed data recorded by maharashtra energy development agency （MEDA） wind farm at Ahmednagar （India）. The main objective is to validate the wind energy probability by using probability distribution function （PDF） of available wind potential. The energy generated from wind for any time interval is equal to the area tinder power curve multiplied by time in hours for that time interval. To estimate the wind energy probability, hourly wind speed data tbr one year interval is selected. Weibull distribution is adopted in this study to best fit the wind speed data. The scale and shape paranleters are estimated by using maximum likelihood method. The goodness of fit tests based on the probability density function （PDF） is conducted to show that the distribution adequately fits the data. It is found from the curve fitting test that, although the two distributions are all suitable for describing the probability distribution of wind speed data, the two-parameter weibull distribution is more appropriate than the lognormal distribution.
