Bayesian Parameter Estimation of Power System Primary Frequency Controls under Modeling Uncertainties*

Nonlinear Bayesian filtering has been utilized in numerous fields and applications. One of the most popular class of Bayesian algorithms is Particle Filters. Their main benefit is the ability to estimate complex posterior density of the state space in nonlinear models. This paper presents the application of particle filtering to the problem of parameter estimation and calibration of a nonlinear power system model. The parameters of interest for this estimation problem are those of a turbine governor model. The results are compared to the performance of a heuristic method. Estimation results have been validated against real-world measurement data collected from staged tests at a Greek power plant.