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6 - Active Sensing for Quickest Anomaly Detection

from Part II - Data-Driven Anomaly Detection

Published online by Cambridge University Press:  22 March 2021

By
Ali Tajer  and
Javad Heydari
Edited by
Ali Tajer ,
Samir M. Perlaza  and
H. Vincent Poor
Ali Tajer
Affiliation:
Rensselaer Polytechnic Institute, New York
Samir M. Perlaza
Affiliation:
INRIA
H. Vincent Poor
Affiliation:
Princeton University, New Jersey
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Summary

This chapter provides an overview of the theory of controlled sensing, and its application to the sequential design of data-acquisition and decision-making processes. Based on the theory, it provides an overview of the applications to the quickest detection and localization of anomalies in power systems. This application is motivated by the fact that 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. Built on the theory of controlled sensing, the chapter discusses a stochastic graphical framework for localizing the anomalies with the minimum amount of data. This framework capitalizes on the strong correlation structures observed among the measurements collected from different buses. This framework, at its core, collects the measurements sequentially and progressively updates its decision about the location of the anomaly.

Keywords

active sensinganomaly detectionstructured dataquickest detection
Type
Chapter
Information
Advanced Data Analytics for Power Systems , pp. 124 - 143
Publisher: Cambridge University Press
Print publication year: 2021

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