This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL). The motivation of the Multi-Stage Cascading Failure (MSCF) problem and its connection with the challenge of climate change are introduced. The bottom-level corrective control of the MCSF problem is formulated based on DCOPF (Direct Current Optimal Power Flow). Then, to mitigate the MSCF issue by a high-level RL-based strategy, physics-informed reward, action, and state are devised. Besides, both shallow and deep neural network architectures are tested. Experiments on the IEEE 118-bus system by the proposed mitigation strategy demonstrate a promising performance in reducing system collapses.
@article{arxiv.2108.10424,
title = {Power Grid Cascading Failure Mitigation by Reinforcement Learning},
author = {Yongli Zhu},
journal= {arXiv preprint arXiv:2108.10424},
year = {2021}
}
Comments
This paper has been accepted by the ICML 2021 Workshop "Tackling Climate Change with Machine Learning". arXiv admin note: substantial text overlap with arXiv:1908.06599