Research Paper#Power Systems, Graph Neural Networks, Data Reconstruction🔬 ResearchAnalyzed: Jan 3, 2026 06:31
GNN with Auxiliary Learning for PMU Data Reconstruction
Published:Dec 31, 2025 01:00
•1 min read
•ArXiv
Analysis
This paper addresses the critical problem of missing data in wide-area measurement systems (WAMS) used in power grids. The proposed method, leveraging a Graph Neural Network (GNN) with auxiliary task learning (ATL), aims to improve the reconstruction of missing PMU data, overcoming limitations of existing methods such as inadaptability to concept drift, poor robustness under high missing rates, and reliance on full system observability. The use of a K-hop GNN and an auxiliary GNN to exploit low-rank properties of PMU data are key innovations. The paper's focus on robustness and self-adaptation is particularly important for real-world applications.
Key Takeaways
- •Proposes a GNN-based method for reconstructing missing PMU data in WAMS.
- •Employs auxiliary task learning to improve accuracy and robustness.
- •Addresses limitations of existing methods, such as concept drift and incomplete observability.
- •Demonstrates superior performance under high missing rates.
Reference
“The paper proposes an auxiliary task learning (ATL) method for reconstructing missing PMU data.”