After years of booming growth on renewable energy, the untapped land suitable for the wind farm becomes increasingly scarce in China. In order to make full use of the land, it became a realistic practice to construct ...After years of booming growth on renewable energy, the untapped land suitable for the wind farm becomes increasingly scarce in China. In order to make full use of the land, it became a realistic practice to construct wind farm together with PV station in those areas where both the wind resource and solar resource are rich. In this paper, based on the analysis of spatial distribution characteristics of wind and solar resources, the factors influencing on the layout of wind turbine and PV array and the interaction between wind turbine and PV array, a proposal for co-siting design wind farm and PV station is discussed.展开更多
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After years of booming growth on renewable energy, the untapped land suitable for the wind farm becomes increasingly scarce in China. In order to make full use of the land, it became a realistic practice to construct wind farm together with PV station in those areas where both the wind resource and solar resource are rich. In this paper, based on the analysis of spatial distribution characteristics of wind and solar resources, the factors influencing on the layout of wind turbine and PV array and the interaction between wind turbine and PV array, a proposal for co-siting design wind farm and PV station is discussed.
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.
