A Bayesian Approach for Parameter Estimation with Uncertainty for Dynamic Power Systems

We address the problem of estimating the uncertainty in the solution of power grid inverse problems within the framework of Bayesian inference. We investigate two approaches, an adjoint-based method and a stochastic spectral method. These methods are used to estimate the maximum a posteriori point of the parameters and their variance, which quantifies their uncertainty. Within this framework we estimate several parameters of the dynamic power system, such as generator inertias, which are not quantifiable in steady-state models. We illustrate the performance of these approaches on a 9-bus power grid example and analyze the dependence on measurement frequency, estimation horizon, perturbation size, and measurement noise. We assess the computational efficiency, and discuss the expected performance when these methods are applied to large systems.