Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids
arXiv:2607.05553v1 Announce Type: new Abstract: 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
The increasing complexity and interconnectedness of smart grids, coupled with the growing potential of AI for real-time control, make robust stability solutions critical now.
This development proposes a new architecture for managing critical infrastructure, leveraging federated learning and multi-agent AI to enhance grid stability and prevent cascading failures.
The approach shifts from traditional centralized control to a distributed, AI-driven paradigm, allowing for more resilient and rapid responses to grid disturbances.
- · Smart Grid Operators
- · AI/ML Developers
- · Energy Infrastructure Providers
- · Power Consumers
- · Legacy Grid Control Systems
- · Centralized Grid Management Paradigms
Increased stability and fewer outages in complex power grids.
Reduced operational costs for grid management and improved energy efficiency.
Accelerated adoption of AI in other critical infrastructure sectors and regulatory frameworks for AI-driven control systems.
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Read at arXiv cs.LG