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.

翻译：数据驱动型状态估计在现代电力系统中日益重要，因为它能够利用实时测量数据更高效地分析系统行为。本文对基于图神经网络（GNN）的相量测量单元（PMU）专用状态估计器（该估计器应用于因子图）进行了全面评估。为评估GNN模型的样本效率，我们针对不同训练集规模开展了多组训练实验。同时，为评估GNN模型的可扩展性，我们在不同规模的电力系统上进行了实验。结果表明，基于GNN的状态估计器兼具高精度和高效数据利用特性，并且在内存占用与推理时间方面均展现出良好的可扩展性，使其成为现代电力系统数据驱动型状态估计的一种前景广阔的解决方案。