7 papers
Assessing the Generalization of Graph Neural Networks for Fault Location Across Increasing Distributed Energy Resource Penetration Levels
Burak Karabulut, Olayiwola Arowolo, Carlo Manna +2
Accurate fault location is critical for distribution network reliability. However, increasing distributed energy resource (DER) penetration complicates fault location due to interm…
Revisiting data-driven dynamic security assessment with a tabular foundation model
Olayiwola Arowolo, Maosheng Yang, Jochen Cremer
Data-driven pre-fault dynamic security assessment (DSA) rapidly evaluates the dynamic risk of credible contingencies on a power system using machine learning. Existing approaches f…
Towards Generalization of Graph Neural Networks for AC Optimal Power Flow
Olayiwola Arowolo, Jochen L. Cremer
AC Optimal Power Flow (ACOPF) is computationally intensive for large-scale grids, often requiring prohibitive solution times with conventional solvers. Machine learning offers sign…
Transferable Graph Learning for Transmission Congestion Management via Busbar Splitting
Ali Rajaei, Peter Palensky, Jochen L. Cremer
Network topology optimization (NTO) via busbar splitting can mitigate transmission grid congestion and reduce redispatch costs. However, solving this mixed-integer nonlinear proble…
Security-Constrained Substation Reconfiguration Considering Busbar and Coupler Contingencies
Ali Rajaei, Jochen L. Cremer
Substation reconfiguration via busbar splitting can mitigate transmission grid congestion and reduce operational costs. However, existing approaches neglect the security of substat…
Addressing Model Inaccuracies in Transmission Network Reconfiguration via Diverse Alternatives
Paul Bannmüller, Périne Cunat, Ali Rajaei +1
The ongoing energy transition places significant pressure on the transmission network due to increasing shares of renewables and electrification. To mitigate grid congestion, trans…