Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimation

2023/02/28 by Ognjen Kundačina, Gorana Gojić, Kundacina, Ognjen +7
Engineering · #Artificial Intelligence (cs.AI) #Energy Load and Power Forecasting #FOS: Computer and information sciences #Machine Learning (cs.LG) #Optimal Power Flow Distribution #Power System Optimization and Stability

paper · pdf · doi:10.48550/arxiv.2303.00105

openalex publication_date 2023/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Related

• Robust and Fast Data-Driven Power System State Estimator Using Graph Neural Networks
• Graph Neural Networks on Factor Graphs for Robust, Fast, and Scalable Linear State Estimation with PMUs
• Physics-informed Graphical Neural Network for Power System State Estimation
• Time-Synchronized State Estimation Using Graph Neural Networks in Presence of Topology Changes
• Distributed Nonlinear State Estimation in Electric Power Systems using Graph Neural Networks