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. 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.
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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.
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IntelliHealer proposes a hierarchical policy network, which can accommodate both discrete and continuous actions.
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MIPLearn provides a set of benchmark problems and random instance generators, covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way.
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MIPLearn is customizable and extensible. For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized.
For installation instructions, basic usage and benchmarks results, see the official documentation.
- Based upon work supported by the U.S. Department of Energy Advanced Grid Modeling Program under Grant DE-OE0000875.
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. Informatics, 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. Informatics},
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}
}
Released under the modified BSD license. See LICENSE for more details.