Power system dynamic state estimation and load modeling

State estimation which constitutes the core of the Energy Management System (EMS), plays an important role in monitoring, control and stability analysis of electric power systems. An efficient, timely and accurate state estimation is a prerequisite for a reliable operation of modern power grids.;Traditional state estimators, which are based on steady state system model, cannot capture the system dynamics very well due to the slow updating rate of SCADA systems. In mid 1980s of the 20th century, Phasor Measurement Unit (PMU)-based Wide-Area Measurement Systems (WAMS) emerged. The introduction of this high speed measurement systems, featured with synchronous sampling, has revolutionized the way state estimation process is being performed. These lead to the development of Dynamic State Estimation (DSE) techniques, which enables the dynamic view of power systems in the control center. Various techniques are available in literature for dynamic state estimation which can be applied to power systems.;In this thesis, the power system dynamic state estimation process, based on Kalman Filtering techniques, is discussed. The dynamic state variables of multi-machine power systems which are generator rotor speed and generator rotor angle are estimated. The computational performance of Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) algorithms in the estimation process of the dynamic state vector of the power systems are compared. The plots of the dynamic state variables, rotor speed and rotor angle, are observed under various transient conditions. It is verified that both EKF and UKF are sufficient techniques in estimation of dynamic state vector elements under transient conditions. Although EKF is one of the most widely used methods in power system dynamic state estimation process, it is investigated that the linearization and Jacobian matrix calculation can lead to some drawbacks. The UKF algorithm which is based on unscented transformation is introduced as a more effective method. It is demonstrated that UKF is easier to implement and more accurate in estimation.;In addition, this thesis describes the load modeling issues in electric power systems. It is an obvious fact that the accuracy of load model is a very important factor effecting the power system stability analysis and control. In this work, the parameter estimation for assumed ZIP load model is performed based on the Weighted Least Square (WLS) estimation method. In order to obtain more reliable and precise calculations of power system state estimation studies, a more accurate load modeling can be developed and integrated into the dynamic state estimation process of power systems as a future work.