Identification of synchronous generator model with frequency control using unscented Kalman filter

•Identification of electromechanical dynamics and frequency control.•Parameter conversion is adopted to improve algorithm convergence.•Event playback method is used in this paper to validate the low-order model.•Real-world PMU data is used for validation.

In this paper, phasor measurement unit (PMU) data-based synchronous generator model identification is carried out using unscented Kalman filter (UKF). The identification not only gives the model of a synchronous generator's swing dynamics, but also gives its turbine-governor model along with the primary and secondary frequency control block models. PMU measurements of active power and voltage magnitude, are treated as the inputs to the system while the measurements of voltage phasor angle, reactive power and frequency are treated as the outputs. UKF-based estimation is carried out to estimate the dynamic states and the parameters of the model. The estimated model is then built and excited with the injection of the inputs from the PMU measurements. The outputs of the estimation model and the outputs from the PMU measurements are compared. Case studies based on PMU measurements collected from a simulation model and real-world PMU data demonstrate the effectiveness of the proposed estimation scheme.