  The decentralization of modern power systems challenges the hierarchical
structure of the electric grid and necessitates automated schemes to manage
adverse conditions. This work proposes an adaptive isolation methodology that
can divide a grid into autonomous islands, ensuring stable and economical
operation amid deliberate (e.g., cyberattacks) or unintentional abnormal
events. The adaptive isolation logic is event-triggered to prevent false
positives, enhance detection accuracy, and reduce computational overhead. A
measurement-based stable kernel representation (SKR) triggering mechanism
initially inspects distributed generation controllers for abnormal behavior.
The SKR then alerts a machine learning (ML) ensemble classifier to assess
whether the system behavior remains within acceptable operational limits. The
event-triggered adaptive isolation framework is evaluated using the IEEE RTS-24
and 118-bus systems. Simulation results demonstrate that the proposed framework
detects anomalous behavior with 100% accuracy in real-time, i.e., within 22
msec. Supply-adequate partitions are identified outperforming traditional
islanding detection and formation techniques while minimizing operating costs.
