  Power grid data are going big with the deployment of various sensors. The big
data in power grids creates huge opportunities for applying artificial
intelligence technologies to improve resilience and reliability. This paper
introduces multiple real-world applications based on artificial intelligence to
improve power grid situational awareness and resilience. These applications
include event identification, inertia estimation, event location and magnitude
estimation, data authentication, control, and stability assessment. These
applications are operating on a real-world system called FNET-GridEye, which is
a wide-area measurement network and arguably the world-largest cyber-physical
system that collects power grid big data. These applications showed much better
performance compared with conventional approaches and accomplished new tasks
that are impossible to realized using conventional technologies. These
encouraging results demonstrate that combining power grid big data and
artificial intelligence can uncover and capture the non-linear correlation
between power grid data and its stabilities indices and will potentially enable
many advanced applications that can significantly improve power grid
resilience.
