Tripping is a common fault in power transmission and distribution systems.Protection measures against tripping used to be relaying operation and electrical component action, which have hysteresis in handling tripping faults.Therefore, the prediction of tripping faults plays a vital role in dealing with hidden problems and power recovery.Here, a method of power system tripping fault prediction based on multisource time series data is proposed.LSTM is used to extract the time characteristics of multisource data, which alleviates the problem of RNN gradient disappearance on long time series.A peephole connection structure is added to the three-layer grid to enable single units to check the LSTM unit status in the previous stage, thereby strengthening the network timing memory capability.Then L2 regularization measures such as parameter normalization are used to mitigate the impact of over fitting in fault prediction.Finally, support vector machine classifier is introduced to improve the generalization ability and robustness of the overall model.The experimental data were obtained from relevant institutions of the State Grid of China.Experiment results show that the proposed method has higher classification accuracy compared with existing data mining methods.The practical application is discussed for its feasibility in actual scenarios.