Scale-Adaptive Multi-task Power Flow Analysis with Local Topology Slicing and Multi-Task Graph Learning

📝 Original Paper Info

- Title: Scale-Adaptive Power Flow Analysis with Local Topology Slicing and Multi-Task Graph Learning
- ArXiv ID: 2601.01387
- Date: 2026-01-04
- Authors: Yongzhe Li, Lin Guan, Zihan Cai, Zuxian Lin, Jiyu Huang, Liukai Chen

📝 Abstract

Developing deep learning models with strong adaptability to topological variations is of great practical significance for power flow analysis. To enhance model performance under variable system scales and improve robustness in branch power prediction, this paper proposes a Scale-adaptive Multi-task Power Flow Analysis (SaMPFA) framework. SaMPFA introduces a Local Topology Slicing (LTS) sampling technique that extracts subgraphs of different scales from the complete power network to strengthen the model's cross-scale learning capability. Furthermore, a Reference-free Multi-task Graph Learning (RMGL) model is designed for robust power flow prediction. Unlike existing approaches, RMGL predicts bus voltages and branch powers instead of phase angles. This design not only avoids the risk of error amplification in branch power calculation but also guides the model to learn the physical relationships of phase angle differences. In addition, the loss function incorporates extra terms that encourage the model to capture the physical patterns of angle differences and power transmission, further improving consistency between predictions and physical laws. Simulations on the IEEE 39-bus system and a real provincial grid in China demonstrate that the proposed model achieves superior adaptability and generalization under variable system scales, with accuracy improvements of 4.47% and 36.82%, respectively.

💡 Summary & Analysis

This paper received essential financial support for conducting research related to smart grids. Particularly, the funding from a major national science and technology project led by the Chinese government and the National Natural Science Foundation of China made this study possible. The author group is primarily active within the School of Electric Power at South China University of Technology, with some authors linked to industrial research institutions. This composition aims to strengthen the connection between academic research and practical application.

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