# Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems

arXiv: https://arxiv.org/abs/2103.14186 · HTML: https://mlsanity.com/paper/2103.14186/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2021.

## Built on by (most cited first, 8 of 8)

- [A Survey of Safe Reinforcement Learning and Constrained MDPs: A Technical Survey on Single-Agent and Multi-Agent Safety](https://mlsanity.com/paper/2505.17342/) (2025)
- [A Review of Safe Reinforcement Learning Methods for Modern Power Systems](https://mlsanity.com/paper/2407.00304/) (2024)
- [A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions](https://mlsanity.com/paper/2508.09128/) (2025)
- [Safe Reinforcement Learning for Power System Control: A Review](https://mlsanity.com/paper/2407.00681/) (2024)
- [Safe Reinforcement Learning for Grid Voltage Control](https://mlsanity.com/paper/2112.01484/) (2021)
- [A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications](https://mlsanity.com/paper/2409.16256/) (2024)
- [Model-Based Exploration in Monitored Markov Decision Processes](https://mlsanity.com/paper/2502.16772/) (2025)
- [Safe Reinforcement Learning-based Automatic Generation Control](https://mlsanity.com/paper/2507.17868/) (2025)

## Abstract (arXiv)

Under voltage load shedding has been considered as a standard and effective measure to recover the voltage stability of the electric power grid under emergency and severe conditions. However, this scheme usually trips a massive amount of load which can be unnecessary and harmful to customers. Recently, deep reinforcement learning (RL) has been regarded and adopted as a promising approach that can significantly reduce the amount of load shedding. However, like most existing machine learning (ML)-based control techniques, RL control usually cannot guarantee the safety of the systems under control. In this paper, we introduce a novel safe RL method for emergency load shedding of power systems, that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Unlike the standard RL method, the safe RL method has a reward function consisting of a Barrier function that goes to minus infinity when the system state goes to the safety bounds. Consequently, the optimal control policy, that maximizes the reward function, can render the power system to avoid the safety bounds. This method is general and can be applied to other safety-critical control problems. Numerical simulations on the 39-bus IEEE benchmark is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.