# Reinforcement Learning for Distributed Transient Frequency Control with Stability and Safety Guarantees

arXiv: https://arxiv.org/abs/2207.03329 · HTML: https://mlsanity.com/paper/2207.03329/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2022.

## Built on by (most cited first, 4 of 4)

- [Safe Reinforcement Learning for Power System Control: A Review](https://mlsanity.com/paper/2407.00681/) (2024)
- [Online Event-Triggered Switching for Frequency Control in Power Grids with Variable Inertia](https://mlsanity.com/paper/2408.15436/) (2024)
- [A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications](https://mlsanity.com/paper/2409.16256/) (2024)
- [Wide-Area Feedback Control for Renewables-Heavy Power Systems: A Comparative Study of Reinforcement Learning and Lyapunov-Based Design](https://mlsanity.com/paper/2511.12911/) (2025)

## Abstract (arXiv)

This paper proposes a reinforcement learning-based approach for optimal transient frequency control in power systems with stability and safety guarantees. Building on Lyapunov stability theory and safety-critical control, we derive sufficient conditions on the distributed controller design that ensure the stability and transient frequency safety of the closed-loop system. Our idea of distributed dynamic budget assignment makes these conditions less conservative than those in recent literature, so that they can impose less stringent restrictions on the search space of control policies. We construct neural network controllers that parameterize such control policies and use reinforcement learning to train an optimal one. Simulations on the IEEE 39-bus network illustrate the guaranteed stability and safety properties of the controller along with its significantly improved optimality.