Deep-learning based fault diagnosis using computer-visualised power flow

Abstract

Changes in system topology, such as branch breaking and the loss of a generator or load, may profoundly influence the operation security of the power system. This study introduces a novel deep-learning based fault diagnosis method using power flow to diagnose topology changes in the power system. Power flow samples with different system states and topologies are first computed numerically; then, they are transformed into computer-visualised images. Using massive power-flow image samples, a convolutional neural network that aims to identify the system state is trained. A feature-map restriction technique is used to restructure the network. To enhance the robustness of the network, the random noise of branch flow is considered in the sample generation process. The results show that the proposed deep-learning based method may diagnose system faults effectively.