This study takes advantage of wide area measurements to propose a centralized nonlinear controller that acts on power system stabilizers, to cooperatively increase the damping of problematic small signal oscillations all over the system. The structure based on decision trees results in a simple, efficient, and dependable methodology that imposes much less computational burden than other nonlinear design approaches, making it a promising candidate for actual implementation by utilities and system operators. Details are given to utilize existing stabilizers while causing minimum changes to the equipment, and warranting improvement or at least no detriment of current system behavior. This enables power system stabilizers to overcome their inherent limitation to act only on the basis of local measurements to damp a single target frequency. This study demonstrates the implications of this new input on mathematical models, and the control functionality that is made available by its incorporation to conventional stabilizers. In preparation of the case of study, a heuristic dynamic reduction methodology is introduced that preserves a physical equivalent model, and that can be interpreted by any commercial software package. The steps of this method are general, versatile, and of easy adaptation to any particular power system model, with the aggregated value of producing a physical model as final result, that makes the approach appealing for industry. The accuracy of the resulting reduced network has been demonstrated with the model of the Central American System. / Ph. D.

Monitoring and protection of power systems is a task that has manifold objectives. Amongst others, it involves performing data mining, optimizing available resources, assessing system stresses, and doing data conditioning. The role of PMUs in fulfilling these four objectives forms the basis of this dissertation. Classification and regression tree (CART) built using phasor data has been extensively used in power systems. The splits in CART are based on a single attribute or a combination of variables chosen by CART itself rather than the user. But as PMU data consists of complex numbers, both the attributes, should be considered simultaneously for making critical decisions. An algorithm is proposed here that expresses high dimensional, multivariate data as a single attribute in order to successfully perform splits in CART. In order to reap maximum benefits from placement of PMUs in the power grid, their locations must be selected judiciously. A gradual PMU placement scheme is developed here that ensures observability as well as protects critical parts of the system. In order to circumvent the computational burden of the optimization, this scheme is combined with a topology-based system partitioning technique to make it applicable to virtually any sized system. A power system is a dynamic being, and its health needs to be monitored at all times. Two metrics are proposed here to monitor stress of a power system in real-time. Angle difference between buses located across the network and voltage sensitivity of buses lying in the middle are found to accurately reflect the static and dynamic stress of the system. The results indicate that by setting appropriate alerts/alarm limits based on these two metrics, a more secure power system operation can be realized. A PMU-only linear state estimator is intrinsically superior to its predecessors with respect to performance and reliability. However, ensuring quality of the data stream that leaves this estimator is crucial. A methodology for performing synchrophasor data conditioning and validation that fits neatly into the existing linear state estimation formulation is developed here. The results indicate that the proposed methodology provides a computationally simple, elegant solution to the synchrophasor data quality problem. / Ph. D.