Power Grid Monitoring Event Recognition Method Integrating Knowledge Graph and Deep Learning - Yang, Yinbin; Hu, Qinran; Liu, Yi; Pan, Xiaohui; Gao, Shang; Hao, Baoxin (Frontiers in Energy Research, 2022)
"Eventization" of power grid monitoring is an effective way to deal with massive alarm information. The existing event recognition method adopts the method of text information mining. However, the alarm vectors generated through this method lack the structural characteristics of monitoring equipment, and the overall recognition accuracy is not high. Therefore, this paper proposes a power grid monitoring event recognition method integrating knowledge graph and deep learning. Firstly, the method constructs the knowledge graph of monitoring equipment and uses the improved GraphSAGE (graph sample and aggregate) algorithm to represent and integrate the structural characteristics of monitoring equipment into the generated alarm vectors. Then, the GRU (Gated Recurrent Unit) neural network trains the alarm vectors and related events to recognize the alarm end to the event end. In addition, this paper combines this method with the monitoring expert system and puts forward the monitoring event recognition strategy by combining the advantages of both. Finally, through the case analysis and comparison of the actual data of the power grid, the effectiveness of the proposed method and strategy is verified, which further improves the accuracy of monitoring event recognition.