Graph neural networks for power grid stability

Abstract

The electric power grid is a very large and complicated system comprised of numerous machines and interconnections. Due to the growing complexity of these connections and increased penetration of renewable energy sources, maintaining stability is becoming an increasingly difficult problem. In recent years, data driven control methods are gaining more attention due to their versatility. Graph neural networks however, offer added stability, computational efficiency and robustness by exploiting the underlying graphical nature of the power grid. This thesis shows applications of graph neural networks to power system analysis. The first application considers the power system security assessment problem. Exploiting the structure of the grid, a classifier to trained to classify safe and unsafe states using a very small network and more efficient training. The classifier is also shown to perform efficiently in cases of limited observability, with missing data at some of the buses in the network. Case studies are shown on the IEEE 68-bus system and the NPCC 140-bus power system. The second application considers under-frequency load shedding using reinforcement, with graph networks improving the speed and computational efficiency of the training process. Results are shown on the IEEE 68-bus power system.