  Having actual models for power system components (such as generators and
loads or auxiliary equipment) is vital to correctly assess the power system
operating state and to establish stability margins. However, power system
operators often have limited information about the actual values for power
system component parameters. Even when a model is available, its operating
parameters and control settings are time-dependent and subject to real-time
identification. Ideally, these parameters should be identified from measurement
data, such as phasor measurement unit (PMU) signals. However, it is challenging
to do this from the ambient measurements in the absence of transient dynamics
since the signal-to-noise ratio (SNR) for such signals is not necessarily
large. In this paper, we design a Bayesian framework for on-line identification
of power system component parameters based on ambient PMU data, which has
reliable performance for SNR as low as five and for certain parameters can give
good estimations even for unit SNR. We support the framework with a robust and
time-efficient numerical method. We illustrate the approach efficiency on a
synchronous generator example.
