Title: Quickest Localization of Anomalies in Power Grids: A Stochastic Graphical Framework,
Abstract: Agile localization of anomalous events plays a pivotal role in enhancing the
overall reliability of the grid and avoiding cascading failures. This is
especially of paramount significance in the large-scale grids due to their
geographical expansions and the large volume of data generated. This paper
proposes a stochastic graphical framework, by leveraging which it aims to
localize the anomalies with the minimum amount of data. This framework
capitalizes on the strong correlation structures observed among the
measurements collected from different buses. The proposed approach, at its
core, collects the measurements sequentially and progressively updates its
decision about the location of the anomaly. The process resumes until the
location of the anomaly can be identified with desired reliability. We provide
a general theory for the quickest anomaly localization and also investigate its
application for quickest line outage localization. Simulations in the IEEE
118-bus model are provided to establish the gains of the proposed approach.