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

TL;DR

This paper introduces a novel scale-adaptive power flow analysis framework using local topology slicing and multi-task graph learning, improving robustness and accuracy across different system scales.

Contribution

It proposes a new framework combining local topology slicing and multi-task graph learning for adaptable and accurate power flow analysis.

Findings

Achieves 4.47% accuracy improvement on IEEE 39-bus system.

Achieves 36.82% accuracy improvement on a real provincial grid.

Demonstrates superior adaptability and generalization across variable system scales.

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.