Grid2Vec: Learning Node Representations of Digital Power Systems for Anomaly Detection

State estimation (SE) plays a crucial role in the monitoring of smart grids. However, as an important cyber-physical system (CPS), smart grid is currently facing various anomalies that pose a serious threat to the accuracy of the state estimation process. This paper proposes a new framework named Grid2Vec (Grid To Vector) for detecting abnormal types in the power system based on graph representation learning. The proposed method involves constructing a statement sequence using a random walk of nodes on the power system graph and training a classification neural network by optimizing the node vector. The trained network can then accurately determine the type of anomaly present in the power system. The proposed Grid2Vec model is scalable to large-scale graph networks and can improve learning efficiency through parallel walking. The effectiveness of the proposed model was verified through numerical experiments conducted on real power systems. The results indicate that the Grid2Vec model exhibits high precision and efficiency in detecting several fault types amongst diverse noise and assault scenarios. It achieves an average detection accuracy of 100.00%, 99.97%, 99.49%, and 99.21%. The level of precision demonstrated is notably superior to the relative error attained by current methodologies.