  Providing situational awareness in light of severe coordinated cyber-attacks
on power grids, where many measurements may be untrusted, is necessary for
reliable monitoring and resilient operation of the grid. In this scenario, the
set of good measurements is by itself insufficient for state estimation due to
loss of observability. In this paper, we present a resilient state estimation
algorithm, based on output clustering. By augmenting the measurement set by
respective cluster variables, the system observability is regained, and a
reliable state estimate can be computed. We show the numerical performance of
our proposed algorithm and its ability to successfully replace corrupted
measurements using cluster variables through an example on the IEEE 24-bus
power system.
