Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis

• URL: http://arxiv.org/abs/2305.00216v1
• Date: Sat, 29 Apr 2023 09:58:15 GMT
• Title: Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis
• Authors: Mei Yang, Gao Qiu, Yong Wu, Junyong Liu, Nina Dai, Yue Shui, Kai Liu, Lijie Ding
• Abstract summary: This letter proposes a physics-guided graph neural network (PG-GNN) for power flow analysis. Case shows that only the proposed method matches AC model-based benchmark, also beats it in computational efficiency beyond 10 times.
• Score: 6.9065457480507995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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