3 papers
eess.SY2026
Robust and Interpretable Graph Neural Networks for Power Systems State Estimation
Arbel Yaniv, Kilian Golinski, Christoph Goebel
This study analyzes Graph Neural Networks (GNNs) for distribution system state estimation (DSSE) by employing an interpretable Graph Neural Additive Network (GNAN) and by utilizing…
cs.LG2025
Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids
Ehimare Okoyomon, Arbel Yaniv, Christoph Goebel
Voltage prediction in distribution grids is a critical yet difficult task for maintaining power system stability. Machine learning approaches, particularly Graph Neural Networks (G…
eess.SY2023
Distribution System Power-Flow Solution by Hierarchical Artificial Neural Networks Structure
Arbel Yaniv, Yuval Beck
In this paper, a new method for solving the power flow problem in distribution systems which is fast, parallel, as well as modular, straightforward, simplified and generic is propo…