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Undetected transmission line outages degrade operator situational awareness and can increase the risk of incorrect control actions and escalating system stress. To improve transmission observability for outage detection under limited monitoring, this paper introduces the Line Outage Impact Factor (LOIF), a new sensitivity indicator that quantifies the relative change in power flow experienced by an observed line following an outage elsewhere. Unlike the conventional Line Outage Distribution Factor (LODF), which normalizes by the pre-outage flow on the disconnected line, LOIF normalizes by the pre-outage flow on the observed line, offering a better indicator for the sensitivity of the observed line to another line’s outage and, thus, facilitating the selection of monitored lines that enhance system observability. Building on LOIF, we propose two observed transmission line (OTL) selection strategies to maximize outage coverage with a small number of monitoring locations. With OTLs selected in the IEEE 30-bus, 118-bus, and the Wisconsin 1664-bus systems, we performed outage detection using different machine learning classifiers. Results show that, for the same number of OTLs, LOIF-based selections generally achieve higher F 1 -scores than LODF-based selections, with the most consistent gains observed in the large 1664-bus system, demonstrating LOIF’s potential for scalable transmission monitoring.
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Flores et al. (2026) studied this question.
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