Power Grid Monitoring Event Recognition Method Integrating Knowledge Graph and Deep Learning

Abstract

“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, 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. First, the method constructs the knowledge graph of monitoring equipment and uses the improved GraphSAGE (graph sample and aggregate) algorithm to perform representation learning on the graph, 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. In addition, this paper combines the proposed method with the existing monitoring expert system, and puts forward a monitoring event recognition strategy. 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.