3 citations · 5 across the 5 of their papers we have counts for
6 papers · 1 filter
Physics-Informed Neural Network Models for EMT Simulators
Ignasi Ventura Nadal, Nicolae Darii, Petros Aristidou +4
This is the first paper, to the best of our knowledge, to propose a framework that integrates Physics-Informed Neural Network (PINN) models in Electromagnetic Transient (EMT) simul…
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…
Physics-Informed Neural Networks in Power System Dynamics: Improving Simulation Accuracy
Ignasi Ventura Nadal, Rahul Nellikkath, Spyros Chatzivasileiadis
The importance and cost of time-domain simulations when studying power systems have exponentially increased in the last decades. With the growing share of renewable energy sources,…
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…
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…
Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow
Rahul Nellikkath, Spyros Chatzivasileiadis
Physics-informed neural networks exploit the existing models of the underlying physical systems to generate higher accuracy results with fewer data. Such approaches can help drasti…