Abstract: Wind power has the characteristics of large fluctuation and strong uncertainty. The time delay and time- space dispersion of the field level ultra short-term outputs on wind conditions and outputs of wind turbines have not been effectively solved. First, for periodic wind direction data, wind direction clustering is performed and wind direction sectors are divided. Secondly, a two-step correlation analysis method is proposed to determine the multivariate characteristic wind speed which is relatively independent and has a great influence on the field level outputs. Then the regression vector is defined based on the finite difference operating domain, and the long short-term memory neural network is used for ultra-short-term time series dynamic modeling. Combined with the non-parametric conditional kernel density estimation and the semi-parameter Copula method the interval model is constructed. Finally, the validity and reliability of the dynamic interval model is verified by a simulation example. This model is suitable for modeling the dynamic response characteristics of wind farms from minutes to seconds, which has guiding significance for the research of fast primary frequency regulation and reactive power voltage regulation of wind farms.