Event-Triggered Islanding in Inverter-Based Grids

TL;DR

This paper tackles automated islanding in inverter-based grids under faults and cyber/intrusion events by coupling a distributed, event-triggered anomaly indicator (Stable Kernel Representation, SKR) with a data-driven islanding topology optimizer. The approach uses SKR alarms at local DG controllers to trigger ensemble ML evaluation (SVMs and bagged trees) that decides whether to form supply-adequate islands, followed by a Mixed Integer Programming (MIP) step to partition the network into stable islands while minimizing load shedding and disconnections. Experiments on IEEE RTS-24 and 118-bus systems show real-time detection (≤0.022 s per decision) with 100% accuracy in the tested scenarios, and the adaptive islanding algorithm forms larger, economically favorable islands even under persistent contingencies. The framework demonstrates scalable, decentralized resilience for DG-integrated grids, with practical potential for deployment on inverter controllers and real-time testbeds, while highlighting the trade-offs between detection speed, accuracy, and operating costs.

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.

Figures (7)

• Figure 1: Overview of the event-triggered islanding framework.
• Figure 2: Response of the SKR event detection scheme during a control input attack targeting DG inverter controller.
• Figure 3: Response of passive islanding detection schemes during a control input attack targeting DG 2, rate of change of: \ref{['fig:ROCOF']}) frequency (ROCOF), \ref{['fig:ROCOV']}) voltage (ROCOV), \ref{['fig:ROCOAP']}) active power (ROCOAP), and \ref{['fig:ROCORP']}) reactive power (ROCORP).
• Figure 4: Voltage and angles of the system during different adverse scenarios.
• Figure 5: Operation of SKR triggering mechanism for different abnormal scenarios, \ref{['fig:PCC']}) three-phase fault at PCC, \ref{['fig:control']}) control input attack at DG 2 , \ref{['fig:LLG']}) line-to-line fault at DG 3, and \ref{['fig:load']}) 50% load deviation attack at DG 4.