🤖 AI Summary
To address the slow response, frequent false tripping, and poor economic efficiency of islanding partitioning in inverter-dominated power grids under abnormal events, this paper proposes an event-triggered adaptive islanding partitioning method. Innovatively, a measurement-driven triggering mechanism based on the Stability Kernel Representation (SKR) is developed and integrated with an ensemble learning classifier to enable collaborative decision-making—achieving high detection accuracy and real-time performance while significantly reducing computational overhead. Validated on the IEEE RTS-24 and 118-bus systems, the method accomplishes 100% anomaly detection and islanding partitioning within 22 ms, improves supply adequacy over conventional approaches, and reduces operational costs. This work establishes an engineering-practical paradigm for enhancing autonomous resilience in power systems with high penetration of power electronics.
📝 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.