5 papers
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…
Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labels
Khai Nguyen, Petros Ellinas, Anvita Bhagavathula +1
To scale optimization and simulation, prior work has explored training machine-learning surrogates that map problem parameters to solutions inexpensively at inference time. Unfortu…
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…
Toolbox for Developing Physics Informed Neural Networks for Power Systems Components
Ioannis Karampinis, Petros Ellinas, Ignasi Ventura Nadal +2
This paper puts forward the vision of creating a library of neural-network-based models for power system simulations. Traditional numerical solvers struggle with the growing comple…