Transient Stability Analysis with Physics-Informed Neural Networks

This repository is the official implementation of Transient Stability Analysis with Physics-Informed Neural Networks.

Environment

To install and activate the environment using conda run:

conda env create -f environment.yml
conda activate pinns_tf_2_4

Code structure

The code is structured in the following way:

• train_model.py contains the entire workflow to train a single model
• power_system_functions.py sets up the power system model, including the parameters and the relevant state equations for simulations and the physics evaluations within the PINN.
• PINN.py defines the neural network model that inherits from the class tensorflow.keras.models.Model
• create_data.py creates a database of trajectories that is used in the selection of the training, validation, and test data. Needs to be run only once.
• dataset_handling.py prepares the data by splitting them and provide the correct format.
• setup_and_run provides a wrapper to setup and run multiple training processes in parallel.

Folder structure

The directory for the storage of all data should contain the following folders and needs to be defined in train_model.py, create_data.py, and setup_and_run:

• datasets
• logs
• result_datasets
• model_weights
• quantiles
• setup_tables

Citation

  @misc{stiasny2021transient,
      title={Transient Stability Analysis with Physics-Informed Neural Networks}, 
      author={Jochen Stiasny and Georgios S. Misyris and Spyros Chatzivasileiadis},
      year={2021},
      eprint={2106.13638},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
  }

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.