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Knowledge of power grids topology during cascading failure is an essential element of centralized blackout prevention control, given that multiple islands are typically formed, as a cascade progresses. Moreover, academic research on interdependency between cyber and physical layers of the grid indicate that power failure during a cascade may lead to outages in communication networks, which progressively reduce the observable areas. These challenge the current literature on line outage detection, which assumes that the grid remains as a single connected component. We propose a new approach to eliminate that assumption. Following an islanding event, first the buses forming that connected components are identified and then further line outages within the individual islands are detected. In addition to the power system measurements, observable breaker statuses are integrated as constraints in our topology identification algorithm. The impact of error propagation on the estimation process as reliance on previous estimates keeps growing during cascade is also studied. Finally, the estimated admittance matrix is used in preventive control of cascading failure, creating a closed-loop system. The impact of such an interlinked estimation and control on that total load served is studied for the first time. Simulations in IEEE-118 bus system and 2,383-bus Polish network demonstrate the effectiveness of our approach.
This paper explores whether graph embedding methods can be used as a tool for analysing the robustness of power-grids within the framework of network science. The paper focuses on the strain elevation tension spring embedding (SETSe) algorithm and co
Cascading failure models are typically used to capture the phenomenon where failures possibly trigger further failures in succession, causing knock-on effects. In many networks this ultimately leads to a disintegrated network where the failure propag
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