Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Stiasny, Jochen"'
Time-domain simulations in power systems are crucial for ensuring power system stability and avoiding critical scenarios that could lead to blackouts. The proliferation of converter-connected resources, however, adds significant additional degrees of
Externí odkaz:
http://arxiv.org/abs/2404.13325
Autor:
Ellinas, Petros, Nellikath, Rahul, Ventura, Ignasi, Stiasny, Jochen, Chatzivasileiadis, Spyros
Verification of Neural Networks (NNs) that approximate the solution of Partial Differential Equations (PDEs) is a major milestone towards enhancing their trustworthiness and accelerating their deployment, especially for safety-critical systems. If su
Externí odkaz:
http://arxiv.org/abs/2402.07621
The ability to accurately approximate trajectories of dynamical systems enables their analysis, prediction, and control. Neural network (NN)-based approximations have attracted significant interest due to fast evaluation with good accuracy over long
Externí odkaz:
http://arxiv.org/abs/2401.05211
Publikováno v:
Electric Power Systems Research, vol. 235, p. 110796, Oct. 2024
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 dynamics. They often require the use of small time step sizes and therefore
Externí odkaz:
http://arxiv.org/abs/2303.10256
Publikováno v:
Electric Power Systems Research, Volume 224, 2023, 109748
The simulation of power system dynamics poses a computationally expensive task. Considering the growing uncertainty of generation and demand patterns, thousands of scenarios need to be continuously assessed to ensure the safety of power systems. Phys
Externí odkaz:
http://arxiv.org/abs/2303.08994
Autor:
Stock, Simon, Stiasny, Jochen, Babazadeh, Davood, Becker, Christian, Chatzivasileiadis, Spyros
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 Neural Networks (BPINNs) combine the advantages of Physics-Informed Neu
Externí odkaz:
http://arxiv.org/abs/2212.11911
Autor:
Hamilton, Robert I., Stiasny, Jochen, Ahmad, Tabia, Chevalier, Samuel, Nellikkath, Rahul, Murzakhanov, Ilgiz, Chatzivasileiadis, Spyros, Papadopoulos, Panagiotis N.
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 showcase the benefits of SHapley Additive exPlanations (SHAP) for und
Externí odkaz:
http://arxiv.org/abs/2209.05793
Autor:
Stiasny, Jochen, Chevalier, Samuel, Nellikkath, Rahul, Sævarsson, Brynjar, Chatzivasileiadis, Spyros
Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represents a challenging paradigm for the power system operators who are taske
Externí odkaz:
http://arxiv.org/abs/2203.07505
Publikováno v:
In Electric Power Systems Research October 2024 235
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 learn the solutions of power system dynamics. In contrast to the limitat
Externí odkaz:
http://arxiv.org/abs/2106.15987