There is a problem of how to use actual fault recorded data to extract and amplify fault feature differences, and carry out fault type and cause identification. Thus a fault identification method for transmission lines based on Gramian angular field (GAF) and transfer learning-ResNet is proposed. First, the distribution characteristics of fault type and cause on transmission lines are analyzed. These are used to guide the construction of fault classifiers suitable for a class imbalance problem. Second, the collected fault voltage and current time sequence signals are converted into GAF images by GAF transform, so that the fault feature differences are amplified as the input of the fault classifier. The generated GAF image set is then fed into an established fault classifier for network training and testing, and the type and cause of transmission line faults are output. Finally, an example analysis using real fault recorded data shows that the proposed method has achieved 97.51% accuracy for fault type identification and 94.23% accuracy for fault cause identification; the trained fault identification network still achieves good fault identification and generalization performance when transferred to other regions. The proposed method provides a novel method for fault identification based on transient waveform data. It can be used for transmission line fault identification in practical power grids.