  This paper presents a novel centralized, variational data assimilation
approach for calibrating transient dynamic models in electrical power systems,
focusing on load model parameters. With the increasing importance of
inverter-based resources, assessing power systems' dynamic performance under
disturbances has become challenging, necessitating robust model calibration
methods. The proposed approach expands on previous Bayesian frameworks by
establishing a posterior distribution of parameters using an approximation
around the maximum a posteriori value. We illustrate the efficacy of our method
by generating events of varying intensity, highlighting its ability to capture
the systems' evolution accurately and with associated uncertainty estimates.
This research improves the precision of dynamic performance assessments in
modern power systems, with potential applications in managing uncertainties and
optimizing system operations.
