6 papers
Multi-Objective Deep Reinforcement Learning for Secure and Stable Power System Operation
Ioannis Papadopoulos, Georgios Tsaousoglou, Johanna Vorwerk
The ongoing energy transition challenges the stable operation of power systems and increases the need for rapid decision-making under uncertainty. While reinforcement learning has…
Tools to Explain Neural Networks for Power System Dynamics
Petros Ellinas, Johanna Vorwerk, Spyros Chatzivasileiadis
This paper presents, for the first time in power systems literature to our knowledge, analytical tools to explain the training performance of machine learning surrogate models for…
Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulation
Petros Ellinas, Indrajit Chaudhuri, Johanna Vorwerk +1
Physics-informed machine learning surrogates are increasingly explored to accelerate dynamic simulation of generators, converters, and other power grid components. The key question…
Neural Operators for Power Systems: A Physics-Informed Framework for Modeling Power System Components
Ioannis Karampinis, Petros Ellinas, Johanna Vorwerk +1
Modern power systems require fast and accurate dynamic simulations for stability assessment, digital twins, and real-time control, but classical ODE solvers are often too slow for…
Neural Networks for AC Optimal Power Flow: Improving Worst-Case Guarantees during Training
Bastien Giraud, Rahul Nellikath, Johanna Vorwerk +2
The AC Optimal Power Flow (AC-OPF) problem is central to power system operation but challenging to solve efficiently due to its nonconvex and nonlinear nature. Neural networks (NNs…
A Dataset Generation Toolbox for Dynamic Security Assessment: On the Role of the Security Boundary
Bastien Giraud, Lola Charles, Agnes Marjorie Nakiganda +2
Dynamic security assessment (DSA) is crucial for ensuring the reliable operation of power systems. However, conventional DSA approaches are becoming intractable for future power sy…