mlsanityData to 14 Oct 2026

Make Safe Decisions in Power System: Safe Reinforcement Learning Based Pre-decision Making for Voltage Stability Emergency Control

arXiv 2405.16485 ↗First posted 26 May 2024eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

The high penetration of renewable energy and power electronic equipment bring significant challenges to the efficient construction of adaptive emergency control strategies against various presumed contingencies in today's power systems. Traditional model-based emergency control methods have difficulty in adapt well to various complicated operating conditions in practice. Fr emerging artificial intelligence-based approaches, i.e., reinforcement learning-enabled solutions, they are yet to provide solid safety assurances under strict constraints in practical power systems. To address these research gaps, this paper develops a safe reinforcement learning (SRL)-based pre-decision making framework against short-term voltage collapse. Our proposed framework employs neural networks for pre-decision formulation, security margin estimation, and corrective action implementation, without reliance on precise system parameters. Leveraging the gradient projection, we propose a security projecting correction algorithm that offers theoretical security assurances to amend risky actions. The applicability of the algorithm is further enhanced through the incorporation of active learning, which expedites the training process and improves security estimation accuracy. Extensive numerical tests on the New England 39-bus system and the realistic Guangdong Provincal Power Grid demonstrate the effectiveness of the proposed framework.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 34 references that are in the map. First posted on arXiv 26 May 2024.

Builds on

11 of 34 references.

References · 11

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  3. 2020A Survey of Deep Active Learningcited by 554
  4. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 371
  5. 2019Lyapunov-based Safe Policy Optimization for Continuous Controlcited by 145
  6. 2020Conservative Safety Critics for Explorationcited by 113
  7. 2023State-wise Safe Reinforcement Learning: A Surveycited by 55
  8. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 50
  9. 2022Evaluating Model-free Reinforcement Learning toward Safety-critical Taskscited by 22
  10. 2021Stability Constrained Reinforcement Learning for Real-Time Voltage Controlcited by 15
  11. 2020Safe Reinforcement Learning for Emergency LoadShedding of Power Systemscited by 8

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  1. 2024A Review of Safe Reinforcement Learning Methods for Modern Power Systems
  2. 2024Safe Reinforcement Learning for Power System Control: A Review
  3. 2024A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications
  4. 2025Safe Reinforcement Learning-based Automatic Generation Control
  5. 2024Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights
  6. 2022Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning
  7. 2026Multi-Objective Deep Reinforcement Learning for Secure and Stable Power System Operation
  8. 2024Learning-Enabled Adaptive Voltage Protection Against Load Alteration Attacks On Smart Grids ↗