Double convolutional neural network for fault identification of power distribution network.

• The double convolutional neural network model for fault data identification is proposed in this paper. The identification rate of distribution network fault types is improved. • A novel convolutional auto-encoding network is used to realize the auto-extraction of the features. This method improves the feature extraction ability and efficiency of the network. • The F 1 score and robustness of the DCNN model were analysed. The optimal structural parameters of DCNN model were determined by parameter sensitivity analysis in discussion. 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 low-dimensional 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. [ABSTRACT FROM AUTHOR]