Topology-Aware Reliability Assessment by Graph Neural Networks

This paper presents a preliminary study on bulk power system reliability assessment using graph neural networks. The proposed method is an end-to-end pipeline that can directly predict the reliability index. The Monte Carlo simulation and the end-to-end machine learning paradigm for bulk power system reliability assessment are introduced. Then, the basic principles of graph signal processing and graph neural networks are explained. Dataset generation and feature engineering for applying graph neural networks on end-to-end reliability assessment under situations of system topology-change are illustrated in detail. Experiment results on the RTS-79 system by the proposed graph neural networks pipeline demonstrate an obvious speed improvement over the regular Monte Carlo simulation method with acceptable prediction errors. Future research directions are also suggested in the final section.