Power system with high penetration of wind power requires much better performance for the accuracy of wind power forecasting (WPF) and reliability of its uncertainty analysis. Most of studies on the WPF uncertainty analysis mainly focus on the static error analysis and modelling for the overall span

and this will limit the model performance and adaptabilities at various time slots and weather conditions. Therefore

a time-switching cloud model is presented for the short-term WPF uncertainty analysis. First

the cloud models of the deterministic forecasting deviation for each selected predictive power range are established in a real-time updating manner. Then

the distribution of cloud drops can be generated according to three mathematical characteristics of the cloud

i.e. expectation

entropy and ultra-entropy. In this way

the uncertainty conditions of given predictive power ranges can be quantified. Moreover

by calculating the quantile of generated cloud drops

the uncertainty forecasting results can be achieved and expressed as the possible power range at given confidence level. The model performance at each time slot could be improved by updating the cloud model according to the current conditions. To take a Chinese wind farm as an example

the results show that the proposed method achieves more reliable uncertain intervals compared with the traditional quantile regression model and it can provide more reliable information for the dispatch and reserve of the power system.