  Since forced oscillations are exogenous to dynamic power system models, the
models by themselves cannot predict when or where a forced oscillation will
occur. Locating the sources of these oscillations, therefore, is a challenging
problem which requires analytical methods capable of using real time power
system data to trace an observed oscillation back to its source. The difficulty
of this problem is exacerbated by the fact that the parameters associated with
a given power system model can range from slightly uncertain to entirely
unknown. In this paper, a Bayesian framework, via a two-stage Maximum A
Posteriori optimization routine, is employed in order to locate the most
probable source of a forced oscillation given an uncertain prior model. The
approach leverages an equivalent circuit representation of the system in the
frequency domain and employs a numerical procedure which makes the problem
suitable for real time application. The derived framework lends itself to
successful performance in the presence of PMU measurement noise, high generator
parameter uncertainty, and multiple forced oscillations occurring
simultaneously. The approach is tested on a 4-bus system with a single forced
oscillation source and on the WECC 179-bus system with multiple oscillation
sources.
