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 distribution network based on double convolutional neural network is proposed in this paper. The 1-dimension convolutional auto-encoder (1-D CAE) is used to learn features from the power grid transient data. The obtained low-dimensional fault features are imported into the 1-dimension convolutional neural network (1-D CNN) identification model. The accurate identification of transient fault data types is achieved by adjusting the parameters of the proposed 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