Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis
Abstract
The increasing scale of alternating current and direct current (AC/DC) hybrid systems necessitates a faster power flow analysis tool than ever. This letter thus proposes a specific physics-guided graph neural network (PG-GNN). The tailored graph modelling of AC and DC grids is firstly advanced to enhance the topology adaptability of the PG-GNN. To eschew unreliable experience emulation from data, AC/DC physics are embedded in the PG-GNN using duality. Augmented Lagrangian method-based learning scheme is then presented to help the PG-GNN better learn nonconvex patterns in an unsupervised label-free manner. Multi-PG-GNN is finally conducted to master varied DC control modes. Case study shows that, relative to the other 7 data-driven rivals, only the proposed method matches the performance of the model-based benchmark, also beats it in computational efficiency beyond 10 times.
Cite
@article{arxiv.2305.00216,
title = {Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis},
author = {Mei Yang and Gao Qiu and Yong Wu and Junyong Liu and Nina Dai and Yue Shui and Kai Liu and Lijie Ding},
journal= {arXiv preprint arXiv:2305.00216},
year = {2023}
}