11 citations · 34 across the 21 of their papers we have counts for
4 papers · 1 filter
Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment
Alyssa Kody, Samuel Chevalier, Spyros Chatzivasileiadis +1
Nonlinear power flow constraints render a variety of power system optimization problems computationally intractable. Emerging research shows, however, that the nonlinear AC power f…
Uncertainty Quantification in LV State Estimation Under High Shares of Flexible Resources
Nils Müller, Samuel Chevalier, Carsten Heinrich +2
The ongoing electrification introduces new challenges to distribution system operators (DSOs). Controllable resources may simultaneously react to price signals, potentially leading…
Learning without Data: Physics-Informed Neural Networks for Fast Time-Domain Simulation
Jochen Stiasny, Samuel Chevalier, Spyros Chatzivasileiadis
In order to drastically reduce the heavy computational burden associated with time-domain simulations, this paper introduces a Physics-Informed Neural Network (PINN) to directly le…
Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers
Samuel Chevalier, Jochen Stiasny, Spyros Chatzivasileiadis
Recent advances in deep learning have allowed neural networks (NNs) to successfully replace traditional numerical solvers in many applications, thus enabling impressive computing g…