Combining Second Order Central Difference Discretization with Extended Kalman Filter for Rotor Speed and Flux Estimation of a Doubly-fed Induction Generator

This paper combines second order central difference discretization method with Extended Kalman Filter (EKF) in order to estimate the rotor speed and flux of a Doubly-Fed Induction Generator (DFIG). A second order discretization yields to a multistep numerical integration method which is known to have better accuracy than single step Euler method. Extended Kalman Filter (EKF) is widely used to estimate the dynamic states of nonlinear system. Usually, a 1st order discretization of the nonlinear system is used with Forward-Euler scheme to obtain a discrete state-space representation, however in this paper, we will combine a second order discretization schemes with EKF in an attempt to get a better estimation of the rotor speed and flux of a DFIG which is widely used in Wind Turbines (WTs). A DFIG modeling in the (dq) reference frame is presented and a description of proposed EKF algorithm is described to estimate the rotor speed and flux. Simulation results are shown and discussed