3 papers
eess.SY2026
Impact of Training Dataset Size for ML Load Flow Surrogates
Timon Conrad, Changhun Kim, Johann Jäger +2
Efficient and accurate load flow calculations are a bedrock of modern power system operation. Classical numerical methods such as the Newton-Raphson algorithm provide highly precis…
cs.LG2025
Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
Changhun Kim, Timon Conrad, Redwanul Karim +6
Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace the classic NewtonRaphson (NR) solvers, especially when thousands of sce…
cs.LG2025
Impact of Data Sparsity on Machine Learning for Fault Detection in Power System Protection
Julian Oelhaf, Georg Kordowich, Changhun Kim +4
Germany's transition to a renewable energy-based power system is reshaping grid operations, requiring advanced monitoring and control to manage decentralized generation. Machine le…