mlsanityData to 14 Oct 2026

Real-Time Cascade Mitigation in Power Systems Using Influence Graph Improved by Reinforcement Learning

arXiv 2506.08893 ↗First posted 10 Jun 2025physics.soc-ph cs.LG physics.data-ancited by 0

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Abstract (arXiv)

Despite high reliability, modern power systems with growing renewable penetration face an increasing risk of cascading outages. Real-time cascade mitigation requires fast, complex operational decisions under uncertainty. In this work, we extend the influence graph into a Markov decision process model (MDP) for real-time mitigation of cascading outages in power transmission systems, accounting for uncertainties in generation, load, and initial contingencies. The MDP includes a do-nothing action to allow for conservative decision-making and is solved using reinforcement learning. We present a policy gradient learning algorithm initialized with a policy corresponding to the unmitigated case and designed to handle invalid actions. The proposed learning method converges faster than the conventional algorithm. Through careful reward design, we learn a policy that takes conservative actions without deteriorating system conditions. The model is validated on the IEEE 14-bus and IEEE 118-bus systems. The results show that proactive line disconnections can effectively reduce cascading risk, and certain lines consistently emerge as critical in mitigating cascade propagation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 36 references that are in the map. First posted on arXiv 10 Jun 2025.

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10 of 36 references.

References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 171
  3. 2017pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systemscited by 85
  4. 2017Model-Free Renewable Scenario Generation Using Generative Adversarial Networkscited by 51
  5. 2019Adaptive Power System Emergency Control using Deep Reinforcement Learningcited by 27
  6. 2015Cascading Power Outages Propagate Locally in an Influence Graph that is not the Actual Grid Topologycited by 9
  7. 2021Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learningcited by 7
  8. 2014An Interaction Model for Simulation and Mitigation of Cascading Failurescited by 6
  9. 2019A Markovian influence graph formed from utility line outage data to mitigate large cascadescited by 5
  10. 2025Analysis and Mitigation of Cascading Failures Using a Stochastic Interaction Graph with Eigen-analysiscited by 1

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  1. 2025Deep Reinforcement Learning for Power Grid Multi-Stage Cascading Failure Mitigation
  2. 2024Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes
  3. 2024RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors
  4. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
  5. 2024Blackout Mitigation via Physics-guided RL
  6. 2024Reinforcement Learning Optimizes Power Dispatch in Decentralized Power Grid
  7. 2024Stochastic Dynamic Power Dispatch with High Generalization and Few-Shot Adaption via Contextual Meta Graph Reinforcement Learning
  8. 2024A Review of Safe Reinforcement Learning Methods for Modern Power Systems