Double convolutional neural network for fault identification of power distribution network

The rapid and accurate identification of different types of faults in the power grid is of great significance to the stable operation of the power grid. An identification model of transient fault recording data for the distribution network based on a double convolutional neural network is proposed in this study. The 1-dimension convolutional auto-encoder (1-D CAE) is used to learn features from the power grid transient data. The obtained lowdimensional fault features are imported into the 1-dimension convolutional neural network (1-D CNN) identification model. The identification accuracy of the proposed model is higher than that of the traditional methods by the verification of the measured transient fault data of the power distribution network. The robustness study implies that the DCNN model can be applied in practical situations prone to using contaminated samples.