Dynamic Power Systems Phasor Estimation Using Kalman Filter Algorithms. In the electrical power system, the accuracy of phasor estimation represents essential and critical issue due to the dependability of many fields on the characteristic of the estimated signals. Therefore, several algorithms have been suggested to estimate the main aspects of these signals. This paper presents a comparative evaluation of dynamic phasor estimation algorithms, namely the linear Kalman and extended Kalman filter. Many tests have been made on the dynamic filters were developed in the Simulink environment of MATLAB, The tests include amplitude step, phase step, frequency step, total vector error and computation time. Test and simulation results are provided to highlight each algorithm suitability and limitations to estimate the phasor of the power system.

Power System Dynamic State Estimation Using Smooth Variable Structure Filter With the integration of distributed energy resources (DER) traditional power systems evolved toward modernized smart grids. Although smart grids open up the possibility for more reliable and secure energy management, they impose new challenges on realtime monitoring and control of the power grid. State estimation is a key function which plays a vital role in reliable system control. In this paper, the smooth variable structure filter (SVSF) is used for power system dynamic state estimation (DSE). SVSF is a predictorcorrector based approach which can be applied to both linear and nonlinear system with the ability to deal with the system uncertainties. The simulation results on a single machine with infinite bus power network shows the superiority of the proposed SVSF compared to extended Kalman filter (EKF) and unscented Kalman filter (UKF). The results of the proposed method show a significant smoothness and accuracy in its performance compared to those obtained from EKF and UKF approaches; in particular, in the presence of measurement outliers.