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

February 09, 2026

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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 ...

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

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