Rebuild AC Power Flow Models with Graph Attention Networks
This work addresses the need for robust power flow analysis in dynamic power systems, though it appears incremental as it builds on existing graph-based methods.
The paper tackles the problem of inaccurate or unavailable power flow model parameters in evolving power networks by proposing a graph attention network-based model to rebuild AC power flow models, achieving better accuracy for changing networks and improved generalization with less accuracy discount compared to state-of-the-art methods.
A full power flow (PF) model is a complete representation of the physical power network. Traditional model-based methods rely on the full PF model to implement power flow analysis. In practice, however, some PF model parameters can be inaccurate or even unavailable due to the uncertainties or dynamics in the power systems. Moreover, because the power network keeps evolving with possibly changing topology, the generalizability of a PF model to different network sizes and typologies should be considered. In this paper, we propose a PF rebuild model based on graph attention networks (GAT) by constructing a new graph based on the real and imaginary parts of voltage at each bus. By comparing with two state-of-the-art PF rebuild models for different standard IEEE power system cases and their modified topology variants, we demonstrate the feasibility of our method. Experimental results show that our proposed model achieves better accuracy for a changing network and can generalize to different networks with less accuracy discount.