COMPREHENSIVE STUDY OF VARIOUS ALGORITHMS FOR DYNAMIC STATE ESTIMATION

Abstract: Precise power system dynamics estimation is essential for improving power system operation, analysis and control. Knowledge of power system dynamics is becoming more important as inverter-based energy sources get more integrated. Therefore, it is necessary to control a power system to evaluate the state variables of a network, but considering the economic confines simultaneous measurement of almost all electrical variables it's impossible. As a result, rather of measuring all of the states using sensors, it is preferable to estimate states. Extended kalman filter (EKF) and Unscented kalman filter (UKF) are used in this work to estimate the dynamic states of the power system (viz. rotor speed and rotor angle). Using WECC 3-machine 9-bus and IEEE 5-machine 14-bus test system, the approaches are validated. EKF and UKF are executed in MATLAB for comparative analysis. A load flow study is carried out initially on the WSCC 9-bus system, and a set of data from the load flow output is used as a measurement input in algorithms. Simulation results are show that the UKF and EKF can accurately estimate the power system dynamics

Keywords: Power system state estimation, extended kalman filter, and unscented kalman filter, ect.