Event-Triggered Islanding in Inverter-Based Grids

The decentralization of modern power systems challenges the hierarchical structure of the electric grid and requires the implementation of automated schemes that can overcome adverse conditions. This work proposes an adaptive isolation methodology that can segregate a grid topology in autonomous islands that maintain stable and economic operation in the presence of deliberate (e.g., cyberattacks) or unintentional abnormal events. The adaptive isolation logic is event-triggered to avoid false positives, improve detection accuracy, and reduce computational overheads. A measurement-based stable kernel representation (SKR) triggering mechanism inspects distributed generation controllers for abnormal behavior. The SKR notifies a machine learning (ML) ensemble classifier that detects whether the system behavior is within acceptable operational conditions. The event-triggered adaptive isolation framework is evaluated using IEEE RTS-24 bus system. Simulation results demonstrate that the proposed framework detects anomalous behavior in real-time and identifies stable partitions minimizing operating costs faster than traditional islanding detection techniques

Fault Detection for Grid-Forming Inverters in Islanded Droop-Controlled AC Microgrids

In this paper, we develop an observer-based fault detection mechanism for grid-forming inverters operating in islanded droop-controlled AC microgrids. The detection scheme uses linear matrix inequalities as constraints with $\mathcal{H}_{-}/\mathcal{H}_{\infty}$ optimization to achieve sensitivity to faults and robustness against disturbances or parametric uncertainties. We explore a nonlinear inverter model formulation based on the less-restrictive one-sided Lipschitz and quadratic inner-boundedness conditions instead of the state-of-the-art formulation based on the Lipschitz condition. In this sense, we aim to overcome the sensitivity of observer-based schemes to the Lipschitz constant. The relation between these two formulations for fault detection is analyzed theoretically. We find the deterministic matrix expressions of different internal faults, including busbar, actuator, and inverter bridge faults. The performance of the proposed detection method is tested on an islanded AC microgrid with four grid-forming inverters and compared against the state-of-the-art nonlinear detection based on the Lipschitz condition. Most importantly, this method requires no additional sensors, a crucial advantage over many proposed solutions in the literature.Comment: 10 pages, 6 figure