Icing flashover fault of transmission line is one of important reasons leading to power grid failure. Current icing flashover researches mainly focus on the model study of flashover voltage of iced insulators. On one hand

the model of flashover voltage cannot fully reflect the insulator flashover voltage under the combined effects of all factors. On the other hand

due to the data acquisition error in the information of the transmission lines

the current characteristics model of icing flashover is difficult to be directly used in the forecasting of icing flashover fault. Considering the development of data mining technology

the partial mutual information(PMI)and support vector machine(SVM)are proposed to predict the icing flashover fault. Firstly

PMI is adopted to select the key factors for input variables. Secondly

SVM forecast model of icing flashover is established to train and predict the sample data. The simulation results show that the forecast method based on the PMI and SVM can predict the icing flashover more effectively

which can act as a reference for the ice defense of power grid.