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Phasor measurement units (PMUs) create ample real-time monitoringopportunities for modern power systems. Among them, line outage detection andidentification remains a crucial but challenging task. Current works on outageidentification succeed in full PMU deployment and single-line outages.Performance however degrades for multiple-line outage with partial systemobservability. We propose a novel framework of multiple-line outageidentification using partial nodal voltage measurements. Using alternatingcurrent (AC) power flow model, phase angle signatures of outages are extractedand used to group lines into minimal diagnosable clusters. Identification isthen formulated into an underdetermined sparse regression problem solved bylasso. Tested on IEEE 39-bus system with 25% and 50% PMU coverage, the proposedidentification method is 93% and 80% accurate for single- and double-lineoutages. Our study suggests that the AC power flow is better at capturingoutage patterns and sacrificing some precision could yield substantialimprovement in identification accuracy. These findings could contribute to thedevelopment of future control schemes that help power systems resist andrecover from outage disruptions in real time.

LASSO-Based Multiple-Line Outage Identification In Partially Observable Power Systems