Research on the identification of high-resistance ground faults in the flexible DC distribution network based on VMD–inception–CNN

With the rapid development of flexible DC distribution networks, fault detection and identification have also attracted people’s attention. High-resistance grounding fault poses a great challenge to the distribution network. The fault current is very small and random, which makes its detection and identification difficult. The traditional overcurrent protection device cannot identify and act on the fault current. Therefore, this paper proposes a fault detection method based on variational mode decomposition (VMD) combined with the convolutional neural network (CNN) of the inception module. This method first uses VMD to decompose the positive transient voltage. Second, it inputs the decomposed signal into CNN for training to obtain the optimal parameters of the model. Finally, the model performance is tested based on the PSCAD/EMTDC simulation platform. Experiments show that the detection method is accurate and effective. It can realize the accurate identification of seven different fault types.