An Interpretable Federated Learning Control Framework Design for Smart Grid Resilience

TL;DR

This paper proposes an interpretable federated learning control framework using ChebyKAN neural controllers to enhance power grid resilience against disturbances, demonstrating faster stabilization in simulations.

Contribution

It introduces a novel federated learning control framework with interpretable neural controllers for power grid stability, emphasizing scalability and resilience.

Findings

Faster stabilization compared to baselines at 10-60% control levels

Scalable and interpretable control solution for power grids

Effective in simulation on IEEE 39-bus system

Abstract

Power systems remain highly vulnerable to disturbances and cyber-attacks, underscoring the need for resilient and adaptive control strategies. In this work, we investigate a data-driven Federated Learning Control (FLC) framework for transient stability resilience under cyber-physical disturbances. The FLC employs interpretable neural controllers based on the Chebyshev Kolmogorov-Arnold Network (ChebyKAN), trained on a shared centralized control policy and deployed for distributed execution. Simulation results on the IEEE 39-bus New England system show that the proposed FLC consistently achieves faster stabilization than distributed baselines at moderate control levels (10\%--60\%), highlighting its potential as a scalable, resilient, and interpretable learning-based control solution for modern power grids.