chenchkx / dkepool-tsa

[IJCNN, 2022] Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System

The Demo of DKEPool Applied in Transient Stability Assessment of Power System

This is the code for our paper "Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System, IJCNN2022". It is based on the code from SOPool. Many thanks!

Created by Kaixuan Chen ([email protected], [email protected])

Download & Citation

If you find our code useful for your research, please kindly cite our paper.

@inproceedings{chen2022distribution,
  title={Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System},
  author={Chen, Kaixuan and Liu, Shunyu and Yu, Na and Yan, Rong and Zhang, Quan and Song, Jie and Feng, Zunlei and Song, Mingli},
  booktitle={2022 International Joint Conference on Neural Networks (IJCNN)},
  year={2022},
  organization={IEEE}
}

@article{chen2022DKEPool,
  title={Distribution Knowledge Embedding for Graph Pooling},
  author={Chen, Kaixuan and Song, Jie and Liu, Shunyu and Yu, Na and Feng, Zunlei and Han, Gengshi and Song, Mingli},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2022}
}

System requirement

Programming language

Python 3.6

Python Packages

PyTorch > 1.0.0, tqdm, networkx, numpy

Run the code

We provide scripts to run the experiments.

For DKEPool module tested on IEEE 39-BUS dataset, run

sh sh_case39.sh