IntelliHealer

IntelliHealer: An imitation and reinforcement learning platform for self-healing distribution networks. IntelliHealer uses imitation learning framework to learn restoration policy for distribution system service restoration so as to perform the restoration actions (tie-line switching and reactive power dispatch) in real time and in embedded environment.

Scope: Training restoration agent	Framework: imitation learning

Such embeddable and computation-free policies allows us to integrate the self-healing capability into intelligent devices A polit project conducted by the S&C Electric can be found here. For details of this work, please refer to our paper at arXiv or IEEE.

Features

• IntelliHealer proposes the imitation learning framework, which improve the sample efficiency using a mixed-integer program-based expert compared with the traditional exploration-dominant reinforcement learning algorithms.

  

• IntelliHealer proposes a hierarchical policy network, which can accommodate both discrete and continuous actions.

  

• IntelliHealer provides an OpenAI-Gym environment for distribution system restoration, which can be connected to Stable-Baselines3, a state-of-the-art collection of reinforcement learning algorithms. Currently, the Gym environment contains two test feeders: 33-node and 119-node system.

Documentation

For installation instructions, basic usage and benchmarks results, see the official documentation.

Acknowledgments

• Based upon work supported by the U.S. Department of Energy Advanced Grid Modeling Program under Grant DE-OE0000875.

Citing IntelliHealer

If you find this code useful in your research, please consider citing:

Y. Zhang, F. Qiu, T. Hong, Z. Wang, and F. Li, “Hybrid imitation learning for real-time service restoration in resilient distribution systems,” IEEE Trans. Ind. Informat., pp. 1-11,early access, 2021, doi: 10.1109/TII.2021.3078110.

@article{Zhang2021_IntelliHealer,
author = {Zhang, Yichen and Qiu, Feng and Hong, Tianqi and Wang, Zhaoyu and Li, Fangxing Fran},
journal = {IEEE Trans. Ind. Informat.},
keywords = {Deep learning,Imitation learning,Mixed-integer linear programming,Reinforcement learning,Resilient distribution system,Service restoration},
pages = {1--11},
note={early access},
title = {{Hybrid imitation learning for real-time service restoration in resilient distribution systems}},
year = {2021}
}

License

Released under the modified BSD license. See LICENSE for more details.