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State estimation plays a key role in the operation of power systems. This role becomes more important considering the increasing demand of emerging power market. Many methods have been proposed for power system state estimation, mostly based on Weighted Least Squares (WLS) approach. However, it is well known that Least Absolute Value (LAV) estimators are more efficient in terms of robustness and accuracy. For these estimators there is no closed form solution and each LAV estimator has its own criteria in choosing desired measurements. In this research, two novel LAV estimators are introduced for power system state estimation. The first estimator employs contraction mapping concepts for rejecting redundant measurements. The second estimator is introduced for systems where sparsity and ill-conditioning occur in the system matrix. In the second estimator, Singular Value Decomposition (SVD) method is combined with contraction mapping technique to find the appropriate equations for the estimation. The application of the new estimator is studied on different IEEE power systems for verification. The estimator shows a robust performance in all the test systems, and the estimation error remains comparatively small even in the presence of significant number of bad data points.
Electric power systems can display a range of undesirable dynamic phenomena by which acceptable, stable operation may be lost; among these is the ``voltage instability" phenomenon. The vulnerability of an electric power system to such phenomenon is typically monitored at intervals of approximately five minutes by state estimators, which are based on a network model to compute the system operating point. The dependence on accurate network parameters and topology, and infrequent monitoring may be regarded as shortcomings in present utility practice. Motivated by this fact, this thesis will propose a conditioning monitor based solely on phasor measurement unit (PMU) data, without the need for network parameters or topology to construct the power flow model and hence suitable for near-real-time implementation. In a very large electric power system, with very large numbers of measurements over a wide geographic area, computational cost may undermine the goal of near-real time computation. Decomposing a large power system into small partitions is considered, to allow the proposed method to provide dependable results in near-real time. This thesis develops a technique for power system decomposition that blends balanced methods with system coherency. Coherency based power system partitioning with consideration of balanced number of buses in parts may result in improved voltage stability assessment and topology change detection of the proposed method with reduced computation time. Actual PMU data may be corrupted or missing, which may cause false alarms to operators. PMU data presents low dimensional behavior, which implies that the PMU data array can be estimated as a sum of small number of rank-1 matrices. Exploiting the low dimensional behavior in PMU data, the arriving PMU data can be checked for its consistency with prior observations. The bad/missing data estimation and correction relies on the premise that aggregate power system loads can be decomposed into a slowly varying, deterministic process and faster time scale stochastic process. This thesis seeks to provide another approach for increasing situational awareness of electric power system by use of a very high resolution data from PMUs, thus exploiting their growing deployment in North America and around the world