  Recent advances in power system State Estimation (SE) have included
equivalent circuit models for representing measurement data that allows
incorporation of both PMU and RTU measurements within the state estimator. In
this paper, we introduce a probabilistic framework with a new RTU model that
renders the complete SE problem linear while not affecting its accuracy. It is
demonstrated that the probabilistic state of a system can be efficiently and
accurately estimated not only with the uncertainties from the measurement data,
but also while including variations from transmission network models. To
demonstrate accuracy and scalability we present probabilistic state estimation
results for the 82k test case that represents the transmission level grid of
the entire USA. It is shown that the estimated state distributions include the
true grid state, while their mean exactly corresponds to the estimated
deterministic state obtained from the nonlinear state estimator.

Signal Processing