Abstract: In order to avoid switching to the fault line, it is necessary to judge the fault state in the closing operation of the outage line. In the double-circuit transmission lines on the same tower, one transmission line operates while the other is in the hot standby state. Thus we proposed a machine learning fault identification method based on induced voltage characteristics of outage lines for the double-circuit transmission line on the same tower. First, the root mean square（RMS） of the induction voltage was measured. The RMS of each phase voltage, the average value of each phase voltage and the fault voltage ratio of each phase were taken as the sample characteristics. We used radial basis kernel function support vector machine（RBF-SVM） to judge the fault state of the outage line. If there is a fault in the line, the BP neural network will be used to identify the fault type. To verify the fault recognition effect of this method, the models of 500 kV double-circuit transmission line on the same tower were established in ATP-EMTP based on the actual data of 6 lines in Hebei Province. The result shows that the identification accuracy of this method for the fault state on the hot standby line is 100% and that for the fault type is 99.7%. This provides a reference for the closing operation and the troubleshooting of the outage line in the dispatching work.