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This paper proposes Extended Kalman Filter (EKF) based dynamic state estimator for power systems using phasor measurement unit (PMU) data. Dynamic state estimation in power systems provides synchronized wide area system history of the dynamic events which is key in the analysis and understanding of the system performance, behavior, and the types of control decisions to be made for large scale power system contingencies. In this paper, 2-axis-fourth-order state space modeling and validation of the synchronous machine is explained in detail. The model is then used for dynamic state estimation using EKF in IEEE 3-Generator-9-Bus Test System. The simulation results show that the model and estimation approach are capable to provide accurate information about the states of the machine and eliminate the noise effects on the measurement signal. The main challenges of dynamic estimation in large power systems are also addressed in this paper. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a scheme for online voltage stability monitoring using an enhanced Radial Basis Function Network (RBFN). A single RBFN is used to predict MW margins for different contingencies. A sequential learning strategy is used along with a regularization technique to design the RBFN and the weights in the output layer are determined by using linear optimization. The proposed network can be adapted with changing operating scenario of the power system. A network pruning strategy is used to limit the growth of the network size due to adaptive training. The proposed scheme is applied on the New England 39-bus power system model. [Copyright &y& Elsevier]