Deep-Adversarial-Transfer Learning Based Fault Classification of Power Lines in Smart Grid

Due to the insufficiency of actual fault samples, the machine learning based fault classification models of the power lines in smart grid are generally trained using the simulated fault samples acquired from software, such as Matlab/Simulink. Yet, the fault features of actual and simulated fault samples are different, and the existed methods might not be valid or accurate enough in classifying the fault types of actual power lines. Thus, a new fault classification model for actual power lines in smart grid based on deep-adversarial-transfer learning is proposed. Firstly, the conditional generative adversarial network (CGAN) is applied for the augmentation of actual fault samples, so as to increase the data amount to some extent. Then, the loss function of convolutional neural network (CNN) is resigned based on transfer learning, and a new fault classification framework based on improved CNN (I-CNN) is proposed. The I-CNN based model is trained using both adversarial and simulated samples, and can make the distrubution of both catergories of samples features closer, thereby achieving to classify the fault types of actual power lines. To verify the method validity, the real-world power line is used for case study. The results show the effectiveness of the proposed method.