Graph Learning Empowered Situation Awareness in Internet of Energy With Graph Digital Twin

Internet of energy (IoE) is one of the most complex industrial systems, and its stable operation is very important. Situation awareness (SA) has been proposed to ensure the stable operation for IoE and making full use of the relationships between components has become the key point for designing an efficient SA model. In this article, graph digital twin (GDT) is proposed by combining digital twin technology with graph theory, to describe the logical relationships between physical entities more accurately in digital space, and then a novel SA model for IoE based on GDT is proposed. In order to make full use of the relationship between nodes, two classifiers based on graph convolution network are designed for fault location and stability prediction. The experimental results show that the proposed SA model can localize the multiple fault components with high accuracy, and can accurately predict the stability of the system.