  In recent decades, the weather around the world has become more irregular and
extreme, often causing large-scale extended power outages. Resilience -- the
capability of withstanding, adapting to, and recovering from a large-scale
disruption -- has become a top priority for the power sector. However, the
understanding of power grid resilience still stays on the conceptual level
mostly or focuses on particular components, yielding no actionable results or
revealing few insights on the system level. This study provides a
quantitatively measurable definition of power grid resilience, using a
statistical model inspired by patterns observed from data and domain knowledge.
We analyze a large-scale quarter-hourly historical electricity customer outage
data and the corresponding weather records, and draw connections between the
model and industry resilience practice. We showcase the resilience analysis
using three major service territories on the east coast of the United States.
Our analysis suggests that cumulative weather effects play a key role in
causing immediate, sustained outages, and these outages can propagate and cause
secondary outages in neighboring areas. The proposed model also provides some
interesting insights into grid resilience enhancement planning. For example,
our simulation results indicate that enhancing the power infrastructure in a
small number of critical locations can reduce nearly half of the number of
customer power outages in Massachusetts. In addition, we have shown that our
model achieves promising accuracy in predicting the progress of customer power
outages throughout extreme weather events, which can be very valuable for
system operators and federal agencies to prepare disaster response.
