Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Transient stability control in smart grids requires rapid post-fault damping of generator frequency and rotor angle deviations to prevent cascading failures. This paper proposes FedPPO-PG, a Federated Multi-Agent Proximal Policy Optimization framework with Physics-Grounded neighborhoods, which reformulates transient stability control as a cooperative multi-agent reinforcement learning problem optimized directly against closed-loop stability objectives. Each generator hosts an independent local actor augmented with the frequency deviations of its two most strongly coupled electrical neighbors, identified from the post-fault Kron-reduced susceptance matrix. A guided policy initialization phase warm-starts all actors from the classical decentralized controller, while a centralized critic guides advantage estimation under the centralized training--decentralized execution (CTDE) paradigm. Evaluated on a simulation of the IEEE 39-bus benchmark system across five training and three unseen fault contingencies, FedPPO-PG achieves 100% stabilization in all 24 trials, reduces mean stability time by 72.4%, and cuts the control power by 7-14 times compared to the centralized baseline. Each actor executes independently with no central coordinator at deployment, and the per-actor inference latency satisfies the IEEE/IEC 60255-118-1-2018 real-time reporting requirements.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 12 references that are in the map. First posted on arXiv 6 Jul 2026.
Builds on
6 of 12 references.
References · 6
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
- 2018Graph Convolutional Reinforcement Learningcited by 152
- 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challengescited by 148
- 2024An Initial Introduction to Cooperative Multi-Agent Reinforcement Learningcited by 6
- 2025An Interpretable Federated Learning Control Framework Design for Smart Grid Resiliencecited by 2
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Citing papers per year: 2026: 1.
1 paper.
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- 2026Engineering Trustworthy Agentic AI for Critical Systems20 Jul 2026
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