Fault Diagnosis Using Deep Convolutional Neural Network with Dynamic Computer-visualised Power Flow

The power flow distribution of the grid changes after fault. This paper proposes a method for transforming the pre-fault and post-fault power flow into a dynamic computer-visualised power flow image (dynamic CVPFI) and using the convolutional neural network (CNN) for fault diagnosis. Firstly, the technical approaches of using CVPFI to identify grid faults are introduced. After that, based on the original CVPFI concept proposed by the author in the early stage, the time-varying characteristics of power flow are added, and the concept of dynamic CVPFI and the method of generating massive dynamic CVPFI sample set are given. Finally, a CNN which conforms to the dimensions of dynamic CVPFI is designed. The case shows that the CNN structure designed in this paper can extract the spatiotemporal features in dynamic CVPFI samples. Compared to the original method, the accuracy of using the dynamic CVPFI for grid fault diagnosis is significantly improved.