Code related to the submission of
- Simon Stock, Jochen Stiasny, Davood Babazadeh, Christian Becker, Spyros Chatzivasileiadis. "[Bayesian Physics-Informed Neural Networks for Robust System Identification of Power System Dynamics] (https://arxiv.org/abs/2212.11911)." arXiv preprint arXiv:2212.11911(2022).
The code is structured in the following way:
run_sys_id.py
runs the system identification, parameters for the SMIB simulation can be definedSMIB_simulation_loop.py
contains the SMIB Simulation loopBPINN
contains all data processing and the BPINN model
@misc{stock2022bpinn,
title={Bayesian Physics-Informed Neural Networks for Robust System Identification of Power System Dynamics},
author={Simon Stock and Jochen Stiasny and Davood Babazadeh and Christian Becker and Spyros Chatzivasileiadis},
year={2022},
eprint={2212.11911},
archivePrefix={arXiv},
primaryClass={eess.SY}
}
The concept of PINNs was introduced by Raissi et al. (https://maziarraissi.github.io/PINNs/) and adapted to power systems by Misyris et al. (https://github.com/gmisy/Physics-Informed-Neural-Networks-for-Power-Systems). The presented code is inspired by these two sources. The concept of BPINNs was introduced by Yang et. al "B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data", Journal of Computational Physics, 2021, https://doi.org/10.1016/j.jcp.2020.109913