The paper presents a unified benchmark of graph neural network (GNN) architectures for predicting AC power flow (ACPF) on IEEE 14‑bus and 118‑bus systems. It isolates the effects of message‑passing mechanisms and inductive biases by using identical preprocessing, training procedures, and comparable parameter budgets. The study finds that anisotropic message‑passing, explicit edge features for line impedances, and global context aggregation yield the best trade‑off between predictive accuracy and physical consistency, while two or three message‑passing layers are generally optimal under a fixed parameter budget.
arXiv:2605.23194v2 Announce Type: replace-cross Abstract: Fast and reliable optimal power flow (OPF) approximation is important for power system operation, yet heterogeneous OPF graph models are ofte...
arXiv:2609.16738v1 Announce Type: cross Abstract: Power Flow (PF), Optimal Power Flow (OPF), and State Estimation (SE) are fundamental problems in power system analysis, but solving them is computati...
arXiv:2607. 13763v1 Announce Type: cross Abstract: Single-task fine-tuning of graph neural networks (GNNs) for power grid problems exhibits a systematic failure mode: models that achieve the lowest in-distribution error degrade the most under topology shift.
Single-task fine-tuning of graph neural networks (GNNs) for power grid problems exhibits a systematic failure mode: models that achieve the lowest in-distribution error degrade the most under topology shift. We term this topology overfitting: the tendency of task-specific gradient signals to encode relational structure particular to the training topologies rather than the underlying physics, causing models to fail on unseen grids despite strong in-distribution performance.
The paper extends the neural network verification framework to graph neural networks by introducing GraphStar sets, which model uncertainty over both node and edge features. This allows sound propagation of linear message‑passing operations and ReLU nonlinearities for GCN and GINE layers. Experiments on power system tasks (PF, OPF, CFA) and graph classification benchmarks (ENZYMES, PROTEINS) show that the method, called GNNV, yields tighter robustness guarantees than CORA and provides, for the first time, edge‑aware guarantees for GINE‑based models under joint node and edge perturbations.
arXiv:2602. 17975v2 Announce Type: replace Abstract: This work formulates and solves optimization problems to generate input points that yield high errors between a neural network's predicted AC power flow solution and solutions to the AC power flow equations.
The paper proposes a hierarchical graph neural network (GNN) architecture for multi‑grid power‑flow modeling that exchanges information through two reduced graphs within a GENCO‑based corrective network. Experiments on three grid topologies show that the Kron‑derived transport approach reduces macro family‑balanced voltage error by 85 % compared to a flat backbone and 31 % compared to a Quotient construction, outperforming per‑bus mean baselines across all seeds. Preliminary results indicate strong generalization across operating scenarios within the studied topologies, though cross‑topology transfer remains an open challenge.
The paper introduces Multi-Channel Physics-Aware Random Walk Fingerprints (MC-PA-RWF), a lightweight graph-level representation that incorporates physical edge states into random-walk propagation for power grid graphs. By constructing multiple edge-weighted channels from domain-relevant attributes and concatenating channel-specific fingerprints, the method achieves high balanced accuracy on PowerGraph benchmarks, outperforming topology-only RWF and matching or surpassing several graph neural network baselines. Experiments on three benchmark systems show statistically significant improvements, with the node-edge extension reaching up to 99.32% balanced accuracy and boosting failure-class F1 scores by 1.60–5.84 percentage points.
arXiv:2608. 16494v1 Announce Type: cross Abstract: Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across a wide range of timescales.
arXiv:2607. 09122v1 Announce Type: new Abstract: Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little attention has been paid to the solution feasibility, which can be obtained by traditional solvers.
arXiv:2410. 04818v2 Announce Type: replace-cross Abstract: We present PINCO, an unsupervised learning framework that integrates Graph Neural Networks with physics-informed neural networks for AC optimal power flow (AC-OPF) solutions.
arXiv:2608. 09921v1 Announce Type: new Abstract: Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power system analysis, where strict physical consistency must be enforced.