Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimation
This addresses data-driven state estimation for power systems, offering a promising incremental improvement with practical benefits.
The paper evaluated a graph neural network (GNN) model for power system state estimation, finding it achieved high accuracy with efficient data usage and demonstrated scalability in memory and inference time across various system sizes.
Data-driven state estimation (SE) is becoming increasingly important in modern power systems, as it allows for more efficient analysis of system behaviour using real-time measurement data. This paper thoroughly evaluates a phasor measurement unit-only state estimator based on graph neural networks (GNNs) applied over factor graphs. To assess the sample efficiency of the GNN model, we perform multiple training experiments on various training set sizes. Additionally, to evaluate the scalability of the GNN model, we conduct experiments on power systems of various sizes. Our results show that the GNN-based state estimator exhibits high accuracy and efficient use of data. Additionally, it demonstrated scalability in terms of both memory usage and inference time, making it a promising solution for data-driven SE in modern power systems.