Estimation of Frequency and Fundamental Power Components Using an Unscented Kalman Filter

Abstract

In this paper, a new method for the simultaneous estimation of power components and frequency is presented. The method is based on the application of the unscented Kalman filter (UKF), which is an estimator capable of estimating the unknown model parameters during severe dynamic changes in the system. A particular advantage of using the UKF is the straightforward estimation procedure, which does not require linearization of the nonlinear signal model. This is an important feature as it improves the accuracy of the method during network transients. The nonlinear state-space parameter model for instantaneous power, taking into account the fundamental components of the system voltages and currents, is used as a starting point for the estimation of the power components and frequency. In the instantaneous power parameter model, the system frequency is considered to be an unknown model parameter, and it is estimated simultaneously with the other unknown model parameters: active and apparent power and the power angle. This resulted in an efficient numerical algorithm for the estimation of power components, which is not sensitive to variations of system frequency. The new estimator has been tested through computer simulations and by using data records obtained under laboratory conditions.