Geometry-Driven Islanding Detection and Fault Classification for Grid-Forming Inverters: A Normally Hyperbolic Invariant Manifold Framework with Physics-Derived... (opens in new tab)

This paper presents a geometry-driven detection and fault-classification framework for grid-forming (GFM) inverters based on normally hyperbolic invariant manifolds (NAIM) and stochastic hypothesis testing. The GFM droop manifold $\mathcal{M}_0$ is identified as a NAIM of the closed-loop dynamics. Transverse fluctuations under grid noise are modeled as an Ornstein--Uhlenbeck process, and the long-run covariance is obtained from the algebraic Lya...