Power Grid Cascading Failure Mitigation by Reinforcement Learning

2021/08/23 by Yongli Zhu, Zhu, Yongli · 1 citation
Engineering · #68T01 #68T07 #Dynamical Systems (math.DS) #F.2.2 #FOS: Computer and information sciences #FOS: Mathematics #K.3.2 #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Power System Optimization and Stability #Power System Reliability and Maintenance #Smart Grid Security and Resilience

paper · pdf · doi:10.48550/arxiv.2108.10424

openalex publication_date 2021/08/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation

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