Unscented Kalman Filter algorithm for on-line identification of parameters of induction motor model

For the nonlinear characteristics of parameter estimation for high-order nonlinear dynamic systems, the Unscented Kalman Filter (UKF) algorithm is introduced. The UKF algorithm description is provided and the probability and statistics features of Unscented Transformation (UT) which uses limited parameters to approximate the random variables are discussed. Also, the error in the traditional estimation by linearization of nonlinear system is avoided. The algorithm is applied to the parameter estimation of induction motor dynamic load model in power systems. Results of case study clearly indicate that the algorithm can quickly and efficiently identify the parameters of this induction motor dynamic load model, and is expected to be implemented in practical engineering operations.