3 citations · 3 across the 3 of their papers we have counts for
4 papers
Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations
Robert I. Hamilton, Jochen Stiasny, Tabia Ahmad +5
Interpretable Machine Learning (IML) is expected to remove significant barriers for the application of Machine Learning (ML) algorithms in power systems. This letter first seeks to…
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…
Capturing Power System Dynamics by Physics-Informed Neural Networks and Optimization
Georgios S. Misyris, Jochen Stiasny, Spyros Chatzivasileiadis
This paper proposes a tractable framework to determine key characteristics of non-linear dynamic systems by converting physics-informed neural networks to a mixed integer linear pr…
Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics
Jochen Stiasny, George S. Misyris, Spyros Chatzivasileiadis
Varying power-infeed from converter-based generation units introduces great uncertainty on system parameters such as inertia and damping. As a consequence, system operators face in…