Fault diagnosis of power grid based on variational mode decomposition and convolutional neural network

The distribution network has complex topological structure and many branches. So, the fault location is easy to be wrongly located. Therefore, a novel hybrid method of combining variational mode decomposition (VMD) and convolutional neural network (CNN) for fault location and fault type identification is proposed in power grid. A fault feature extraction method based on VMD and Hilbert-Huang transform (HHT) is designed. In this method, the VMD is used to analyze the characteristic features from fault transient signals of the positive sequence current. The fault features of the intrinsic mode function with more fault features are extracted through HHT. The extracted fault feature vector is used as the input of CNN to build fault diagnosis model. Finally, the fault diagnosis report is obtained by comparing and analyzing the output results of SoftMax layer. The experimental results show that this method can identify the fault location and type in the small current grounding power system model of relay protection dynamic simulation equipment. Meanwhile, the method is less influenced by fault resistance and fault distance and has good accuracy, thus having better accuracy and generalization ability than traditional methods.