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