A GL-CN framework deeply integrates physics-guided and physics-constrained knowledge into a dual-channel GCN-MLP model to deliver fast, accurate, and robust frequency security assessment for power grids with high renewable penetration.
Prediction for the maximum frequency deviation of post-disturbance based on the deep belief network[C]//2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia)
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Knowledge-data fusion framework for frequency security assessment in low-inertia power systems
A GL-CN framework deeply integrates physics-guided and physics-constrained knowledge into a dual-channel GCN-MLP model to deliver fast, accurate, and robust frequency security assessment for power grids with high renewable penetration.