Quickest change detection with applications to line outage detection

In this work, we focus on applications of quickest change detection (QCD) theory in the problem of line outage detection and identification. We start by discussing fundamental results of sequential hypothesis testing and QCD, and by proposing an algorithm for the QCD setting under transient dynamics. Following, we apply these results in the line outage detection problem. QCD algorithms are applied on measurements of voltage phase angles, which are collected using phasor measurement units (PMUs), sampling units that sample at an approximate rate of 30 samples per second and that are placed in the buses of the system. The goal is to detect a line outage as fast as possible, under false alarm constraints. First, we study the line outage setting where no transient dynamics are present. Then, we propose a QCD algorithm for the case where transient dynamics are present. Line outage identification schemes are also discussed

Detecting an Intermittent Change of Unknown Duration

Oftentimes in practice, the observed process changes statistical properties at an unknown point in time and the duration of a change is substantially finite, in which case one says that the change is intermittent or transient. We provide an overview of existing approaches for intermittent change detection and advocate in favor of a particular setting driven by the intermittent nature of the change. We propose a novel optimization criterion that is more appropriate for many applied areas such as the detection of threats in physical-computer systems, near-Earth space informatics, epidemiology, pharmacokinetics, etc. We argue that controlling the local conditional probability of a false alarm, rather than the familiar average run length to a false alarm, and maximizing the local conditional probability of detection is a more reasonable approach versus a traditional quickest change detection approach that requires minimizing the expected delay to detection. We adopt the maximum likelihood (ML) approach with respect to the change duration and show that several commonly used detection rules (CUSUM, window-limited CUSUM, and FMA) are equivalent to the ML-based stopping times. We discuss how to choose design parameters for these rules and provide a comprehensive simulation study to corroborate intuitive expectations.Comment: 34 pages, 7 figures, 6 table