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

@article{Misyris2021CapturingPS, title={Capturing Power System Dynamics by Physics-Informed Neural Networks and Optimization}, author={Georgios S. Misyris and Jochen Stiasny and Spyros Chatzivasileiadis}, journal={2021 60th IEEE Conference on Decision and Control (CDC)}, year={2021}, pages={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 number of simulations and other heuristics to determine parameters such as the critical clearing time, i.e., the maximum allowable time within which a disturbance must be cleared before the system moves to…

## 2 Citations

Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow to Mixed-Integer Linear Programs

- Computer ScienceArXiv
- 2020

This paper introduces a framework to capture previously intractable optimization constraints and transform them to a mixed-integer linear program, through the use of neural networks, and demonstrates the approach for power system operation considering N-1 security and small-signal stability.

Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems

- Computer ScienceArXiv
- 2022

This work outlines key challenges (dataset generation, data pre-processing, model training, model assessment, and model embedding) associated with building trustworthy ML models which learn from physics-based simulation data and implements methods that connect different elements of the learning pipeline through feedback, thus “closing the loop” between model trainings, performance assessments, and re-training.

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