Author

Jin-Bao Wang

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Open access Oct 2026

Hierarchical Graph Neural Network for Probabilistic Power Flow With Grid Topology Contingencies

Data-driven surrogates reduce the nonlinear solves required for probabilistic power flow (PPF), but models tied to a fixed bus--branch graph often generalize poorly when outages change electrical coupling. This paper proposes a topology-decomposition-driven hierarchical bidirectional graph neural network (TDD--HBGNN) f...

Jin-Bao Wang, Jun Liu, Shi-Liang Zhang et al. · IEEE Transactions on Industr... · 0 citations

Open access Sep 2026

Measurement-Based Probabilistic Power Flow Using a Basis Constrained Graph Convolutional Network with Few-Shot Node Adaptation

Probabilistic power flow quantifies voltage and phase angle uncertainty under variable photovoltaic generation, but repeated AC Monte Carlo simulation is costly. A local topology change also modifies the electrical operator and state dimension when only a small target data set is available. We propose a Basis Constrain...

Jin-Bao Wang, Jun Liu, Hao-Bo Zhang et al. · Italian National Conference... · 0 citations