Event-triggered islanding in inverter-based grids

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 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 an ensemble classifier to assess whether the system behavior remains within acceptable operational limits. The event-triggered adaptive isolation framework is evaluated using 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 ms. Supply-adequate partitions are identified outperforming traditional islanding detection and formation techniques while minimizing operating costs. • Event-triggered adaptive isolation method for stable, autonomous grid operation. • Application-agnostic method to address cyberattacks and unintentional maloperations. • Anomaly detection using Stable Kernel Representation-assisted ML ensemble classifiers. • Island formation of supply-adequate grid partitions while reducing operational costs. • Islanding framework validation on IEEE RTS-24 and 118-bus systems.