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10 items by relevance
1 Conference paper

Physics-Informed Neural Networks for Power Systems

Misyris, Georgios; Venzke, Andreas; Chatzivasileiadis, Spyros

Proceedings of 2020 Ieee Power & Energy Society General Meeting — 2020, pp. 1-5

This paper introduces for the first time, to our knowledge, a framework for physics-informed neural networks in power system applications. Exploiting the underlying physical laws governing power systems, and inspired by recent developments in the field of machine learning, this paper proposes

Year: 2020

Language: English

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2 Preprint article

Physics-Informed Neural Networks for Power Systems

Misyris, George S.; Venzke, Andreas; Chatzivasileiadis, Spyros

This paper introduces for the first time, to our knowledge, a framework for physics-informed neural networks in power system applications. Exploiting the underlying physical laws governing power systems, and inspired by recent developments in the field of machine learning, this paper proposes

Year: 2020

Language: Undetermined

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3 Conference paper

Capturing Power System Dynamics by Physics-Informed Neural Networks and Optimization

Misyris, Georgios S.; Stiasny, Jochen; Chatzivasileiadis, Spyros

2021 60th Ieee Conference on Decision and Control (cdc) — 2021, pp. 4418-4423

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 program. Our focus is on power system applications. Traditional methods in power systems require the use of a large

Year: 2021

Language: English

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4 Conference paper

Capturing Power System Dynamics by Physics-Informed Neural Networks and Optimization

Misyris, Georgios S.; Stiasny, Jochen; Chatzivasileiadis, Spyros

Proceedings of 60<sup>th</sup> Ieee Conference on Decision and Control — 2021, pp. 4418-4423

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 program. Our focus is on power system applications. Traditional methods in power systems require the use of a large

Year: 2021

Language: English

More...
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5 Preprint article

Capturing Power System Dynamics by Physics-Informed Neural Networks and Optimization

Misyris, Georgios S.; Stiasny, Jochen; Chatzivasileiadis, Spyros

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 program. Our focus is on power system applications. Traditional methods in power systems require the use of a large

Year: 2021

Language: Undetermined

More...
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6 Conference paper

Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics

Stiasny, Jochen; Misyris, George S.; Chatzivasileiadis, Spyros

Proceedings of 2021 Ieee Madrid Powertech — 2021, pp. 1-6

the widespread deployment of phasor measurement units (PMUs) and aiming at developing a fast dynamic state and parameter estimation tool, this paper investigates the performance of Physics-Informed Neural Networks (PINN) for discovering the frequency dynamics of future power systems. PINNs have the potential

Year: 2021

Language: English

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7 Preprint article

Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics

Stiasny, Jochen; Misyris, George S.; Chatzivasileiadis, Spyros

the widespread deployment of phasor measurement units (PMUs) and aiming at developing a fast dynamic state and parameter estimation tool, this paper investigates the performance of Physics-Informed Neural Networks (PINN) for discovering the frequency dynamics of future power systems. PINNs have the potential

Year: 2021

Language: Undetermined

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8 PhD Thesis

Towards Zero-Inertia Power Systems: Stability Analysis, Control & Physics-Informed Neural Networks

Misyris, Georgios

Technical University of Denmark — 2021

algebraic equations. This thesis proposes, for the first time, physics informed neural networks for power system applications and demonstrates how they can provide solutions for a system of differential algebraic equations at a fraction of the time required by traditional numerical solvers, while maintaing

Year: 2021

Language: English

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