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 PMU-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.

本论文针对基于图神经网络应用于因子图的相量测量装置的状态估计器进行了全面评估，通过多种训练集大小的实验及不同规模的电力系统进行实验，结果表明该算法在提高系统行为分析效率方面具有高精度、高效率、低内存占用和低推断时间的特点。

电力系统状态估计中图神经网络的可扩展性和样本效率分析