<b>Multi-Agent Reinforcement Learning via Mean Field Game for Power Grid Fault Diagnosis & Prediction</b>

This paper proposes a novel framework integrating MARL with MFG theory to address the inherent scalability limitations of conventional multi-agent approaches in large-scale power systems. Each monitoring agent observes local grid states and interacts with the collective behavior of neighboring agents through a mean field approximation, substantially reducing the computational complexity from exponential to linear scaling with respect to the number of agents.

The fault diagnosis and prediction problem are formulated as a stochastic differential game and derive the corresponding mean field equilibrium conditions. Extensive experiments conducted on the IEEE 14-bus, 39-bus, and 118-bus benchmark systems demonstrate that the MFG-MARL framework achieves a fault detection accuracy of 97.3%, 96.1%, and 94.8%, a false positive rate of 1.8%, 2.1%, and 2.7%, separately and prediction lead time of 450 milliseconds.