Based onthe analysis on the different regimes in the volatility of wind power time series, a prospective wind power forecasting method based on Multiple Outlier Smooth Transition Autoregressive (MOSTAR) type models is presented. By modifying Conditional Maximum Likelihood Estimate (CMLE), the parameters of the MOSTAR models are obtained. Considering the fat-tail effect in the volatility of wind power time series, MOSTAR models with fat-tail distribution are proposed for generalization. Moreover, with the regime switching parameter of the proposed model, the multiple outlier effect of real case is depicted more rigorously. In addition, Standard News Impact Surface (SNIS), a refined tool for volatility analysis is provided to analyze the dynamic varying characteristics of conditional variance. Case studies on a practical wind power data validate the feasibility and effectiveness of M-OSTAR type model. This work is supported byNational Natural Science Foundation of China (No. 51577025) and Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 14KJB470003).