A Knowledge Graph-Based Method for Predicting Substation Accident Hazards

Major accidents at substations are generally caused by a variety of safety hazards. However, the interactions between the various hazards within the substation, which result in a heterogeneous network of relationships, make it difficult for traditional generalised analytical models to accurately predict hidden dangers. To solve this problem, we propose a knowledge graph-based method for predicting substation accident hazards, designed to block potential accident causal pathways. Based on the knowledge graph of substation accident hazards, we enhance the network's understanding of anomalies in the complex substation environment through rule-based reasoning, while also leveraging the strong nonlinear fitting capabilities of neural networks to deeply explore the hidden relationships in the data, enabling the risk prediction of substation hazards and preventing major accidents. We validated our model using data from a provincial grid company over the past five years and achieved a prediction accuracy of 80%. This fully demonstrates the effectiveness of our approach in improving the prediction accuracy of substation hazards, and highlights its significant theoretical and practical value in enhancing the safety and stability of the entire substation system.