Event-Triggered Islanding in Inverter-Based Grids

TL;DR

This paper introduces an adaptive, event-triggered islanding method for inverter-based power grids that quickly detects abnormal conditions and isolates grid sections to maintain stability and reduce costs.

Contribution

It proposes a novel adaptive isolation framework combining SKR and ensemble classifiers for real-time detection and partitioning of power grids.

Findings

Detects abnormal behavior with 100% accuracy within 22 milliseconds.

Outperforms traditional islanding detection methods in speed and cost efficiency.

Successfully applied to IEEE RTS-24 and 118-bus systems.

Abstract

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 22msec. Supply-adequate partitions are identified outperforming traditional islanding detection and formation techniques while minimizing operating costs.