Research in the area of power system transient stability has recently focused on dynamic state estimation using high rate Phasor Measurement Unit (PMU) data. Several mathematical models for synchronous machine are developed and various estimation approaches are proposed for this purpose. In this thesis, the mathematical formulation of nonlinear state space modeling and the principles of Kalman Filter are explained. Extended and Unscented Kalman Filters (EKF and UKF), as two nonlinear estimation methods, are applied for state and parameter estimation in an induction motor. In the next stage, after presenting a thorough explanation about modeling of the synchronous machine, dynamic state estimation is applied on different power system case studies and the results of estimation methods are compared. The simulation results provided in this thesis show the great potential of the proposed estimation approaches for accurately estimating the states of the machine as well as reducing the effect of noise on input signals
