Initial Development of Automatic Situation Awareness System Using Dynamic Graph Inference

Knowledge modeling, representation, and automatic knowledge inference are fundamental problems for developing automatic situation awareness (ASA) systems in the field of complex power system rescheduling. In this paper, we present a knowledge representation model named dynamic rescheduling graph (DRG), i.e., a new kind of relational knowledge graph, to represent the knowledge of the power dispatchers. DRG has many advantages, such as using different knowledge representation methods like function and logic expression and setting attributes to nodes and edges, which make it easier to develop an ASA system for complex situation diagnose, event solving, and rescheduling. To make inference timely in the DRG, we develop a unique algorithm called hybrid mapping inference (HMI), which supports forward, backward, and mapping inference. We also build an initial ASA system based on the proposed knowledge model and the inference algorithm. We validate the effectiveness of the ASA system in the Guangzhou regional power grid.