Mining the inherent persistence property of the time series of wind power is crucial for forecasting and controlling wind power.Few common methods exist that can fully depict and quantify the persistence property.Base...Mining the inherent persistence property of the time series of wind power is crucial for forecasting and controlling wind power.Few common methods exist that can fully depict and quantify the persistence property.Based on the definition of the active power output state of a wind farm,this paper describes the statistical persistence property of the duration time and state transition.Based on the results of our analysis of significant amounts of wind power field measurements,it is found that the duration time of wind power conforms to an inverse Gaussian distribution.Additionally,the state transition matrix of wind power is discovered to yield a ridge property,the gradient of which is related to the time scale of interest.A systemaic methodology is proposed accordingly,allowing the statistical characteristics of the wind power series to be represented appropriately.展开更多
基金supported by the Natural High Technology Research and Development of China(863 Program)(Grant No.2011AA05A112)the National Natural Science Foundation of China(Grant No.51377027)ABB(China)Ltd.
文摘Mining the inherent persistence property of the time series of wind power is crucial for forecasting and controlling wind power.Few common methods exist that can fully depict and quantify the persistence property.Based on the definition of the active power output state of a wind farm,this paper describes the statistical persistence property of the duration time and state transition.Based on the results of our analysis of significant amounts of wind power field measurements,it is found that the duration time of wind power conforms to an inverse Gaussian distribution.Additionally,the state transition matrix of wind power is discovered to yield a ridge property,the gradient of which is related to the time scale of interest.A systemaic methodology is proposed accordingly,allowing the statistical characteristics of the wind power series to be represented appropriately.
