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

Code related to the submission of

• Jochen Stiasny and George S. Misyris and Spyros Chatzivasileiadis. "Physics-Informed Neural Networks for Non-linear System Identification applied to Power System Dynamics." arXiv preprint arXiv:2004.04026 (2020).

Code structure

The code is structured in the following way:

• run_system_identification.py contains the entire workflow
• create_example_parameters.py creates a dictionary with all relevant information about the system and the training which are used throughout the process
• create_data.py to showcase the method in the absence of measurement data, this function creates the training data
• ode_solver.py a simple ode-solver used in create_data.py
• PinnModel.py the network model that inherits from the class tensorflow.keras.models.Model
• PinnLayer.py the layer (inheriting from tensorflow.keras.layers.Layer) that combines the dense neural network with the automatic differentiation

Citation

@misc{stiasny2020physicsinformed,
    title={Physics-Informed Neural Networks for Non-linear System Identification applied to Power System Dynamics},
    author={Jochen Stiasny and George S. Misyris and Spyros Chatzivasileiadis},
    year={2020},
    eprint={2004.04026},
    archivePrefix={arXiv},
    primaryClass={eess.SY}
}

Related work

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.