45 citations · 101 across the 13 of their papers we have counts for
13 papers
Physics-Informed Neural Networks: a Plug and Play Integration into Power System Dynamic Simulations
Ignasi Ventura Nadal, Jochen Stiasny, Spyros Chatzivasileiadis
Time-domain simulations are crucial for ensuring power system stability and avoiding critical scenarios that could lead to blackouts. The next-generation power systems require a si…
Correctness Verification of Neural Networks Approximating Differential Equations
Petros Ellinas, Rahul Nellikath, Ignasi Ventura +2
Verification of Neural Networks (NNs) that approximate the solution of Partial Differential Equations (PDEs) is a major milestone towards enhancing their trustworthiness and accele…
Error estimation for physics-informed neural networks with implicit Runge-Kutta methods
Jochen Stiasny, Spyros Chatzivasileiadis
The ability to accurately approximate trajectories of dynamical systems enables their analysis, prediction, and control. Neural network (NN)-based approximations have attracted sig…
PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks
Jochen Stiasny, Baosen Zhang, Spyros Chatzivasileiadis
The dynamic behaviour of a power system can be described by a system of differential-algebraic equations. Time-domain simulations are used to simulate the evolution of these dynami…
Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibility
Jochen Stiasny, Spyros Chatzivasileiadis
The simulation of power system dynamics poses a computationally expensive task. Considering the growing uncertainty of generation and demand patterns, thousands of scenarios need t…
Bayesian Physics-Informed Neural Networks for Robust System Identification of Power Systems
Simon Stock, Jochen Stiasny, Davood Babazadeh +2
This paper introduces for the first time, to the best of our knowledge, the Bayesian Physics-Informed Neural Networks for applications in power systems. Bayesian Physics-Informed N…
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…
Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems
Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath +2
Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represen…
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…
Transient Stability Analysis with Physics-Informed Neural Networks
Jochen Stiasny, Georgios S. Misyris, Spyros Chatzivasileiadis
We explore the possibility to use physics-informed neural networks to drastically accelerate the solution of ordinary differential-algebraic equations that govern the power system…
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…
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…