Enhancing Electrical Grid Reliability through Predictive Cycle Detection with Graph Neural Networks

Abstract

This paper represents end-end study focused on improving the grid reliability with the application of Graph Neural Networks (GNNs). Graph Representation of the electrical grid which yields the model of nodes of substations and transformers interconnection of power lines constructed by the data from the National Grid Electricity System Operator (ESO) Data Portal. Based on their connections, node feature updating and encoding by predict grid reliability with multi-layered Graph Attention Network (GAT) was employed. In predicting failure regions, proposed model with rigorously trained and tested state shows higher accuracy compared to existing methods. Results of the model signifies the model capability to efficiently manage large-scale data with actionable insight generation for specific usecases such as predictive maintanance, which ensure the resilience of modern power systems and integrating renewable energy in the modern power system.