Abstract
Power system state estimation is not only the foundation of power system dispatch, control and security assessment, but also the core of Energy Management System. The purpose of state estimation is using the measurements and the grid topology information to get the real time state of the power system. State estimation is typically performed using the Kalman filter method. Therefore, this project uses the Kalman filter method to solve the estimation problem in power systems. Along with the derivation of the Kalman filter algorithm, the principle of the extended Kalman filter method and its shortcomings were introduced. Then a relatively new Kalman filter, the unscented Kalman filter is discussed. This filter avoids the complexity and lengthy calculations of the derivative and also increases the precision of estimation. The simulation is done in MATLAB using models of systems from several published research papers. This report uses single machine and multi-machine systems as study cases to evaluate the performance of both methods. The results show that both methods did an excellent job in tracking the system.