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… 

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