# Power Grid Cascading Failure Mitigation by Reinforcement Learning

arXiv: https://arxiv.org/abs/2108.10424 · HTML: https://mlsanity.com/paper/2108.10424/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [An Interaction Model for Simulation and Mitigation of Cascading Failures](https://mlsanity.com/paper/1401.0750/) (2014)
- [Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification](https://mlsanity.com/paper/1812.09427/) (2018)

## Built on by (most cited first, 1 of 1)

- [Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation](https://mlsanity.com/paper/2505.09012/) (2025)

## Abstract (arXiv)

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.