Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
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3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Introduces Forming Index for device-level GFM quantification and multi-bus voltage stiffness metric for system strength, with a formal proof that GFM converters improve strength.
Loop shaping transformations reformulate converter and network models to resolve non-sectoriality at low frequencies, extending mixed gain-phase conditions for decentralized stability certification of grid-forming converters.
citing papers explorer
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Learning Dynamic Stability Landscapes in Synchronization Networks
Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
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Quantifying Grid-Forming Behavior: Bridging Device-Level Dynamics and System-Level Strength
Introduces Forming Index for device-level GFM quantification and multi-bus voltage stiffness metric for system strength, with a formal proof that GFM converters improve strength.
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Decentralized Small Gain and Phase Stability Conditions for Grid-Forming Converters: Limitations and Extensions
Loop shaping transformations reformulate converter and network models to resolve non-sectoriality at low frequencies, extending mixed gain-phase conditions for decentralized stability certification of grid-forming converters.