This thesis presents a novel technique for prediction of the transient stability status of a power system following a large disturbance such as a fault, and application of the tech-nique for subsequent emergency control. The prediction is made based on the synchro-nously measured samples of the magnitudes of fundamental frequency voltage phasors at major generation/load centers. The voltage samples are taken immediately after a fault is cleared and used as inputs to a binary classifier based on support vector machines to iden-tify the transient stability condition. The classifier is trained using examples of the post-fault recovery voltages (inputs) obtained through simulations and the corresponding sta-bility status (output) determined using a power angle-based stability index. Studies with the New England 39-bus test system indicate that the proposed algorithm can correctly recognize when the power system is approaching transient instability. The proposed sys-tem is then applied to Venezuelan power system and Manitoba Hydro power grid to demonstrate the applicability for large practical power systems. Performance of the pro-posed transient stability prediction scheme under the presence of asymmetrical faults, voltage sensitive loads, unlearned network topologies and measurement noise was found to be satisfactory. Once an impending transient instability situation has been detected, appropriate emer-gency control strategies are triggered to minimize the impact of this on the safe operation of the network and reduce the possibility of a blackout. This thesis examines two differ-ent emergency control schemes: a) A fuzzy logic based emergency load and generator shedding scheme and b) A high voltage direct current (HVdc) power order reduction scheme based on synchronized phasors measurements. These strategies were developed for two power systems with contrasting characteristics: one for the Venezuelan power system which is a conventional power system completely based on alternating current (AC) transmission, and the other for the Manitoba Hydro network which heavily depend on long HVdc transmission for power transfer. The proposed wide area control systems demonstrated good performance on the Venezuelan and Manitoba Hydro power grids.

This thesis presents a novel technique for prediction of the transient stability status of a power system following a large disturbance such as a fault, and application of the tech-nique for subsequent emergency control. The prediction is made based on the synchro-nously measured samples of the magnitudes of fundamental frequency voltage phasors at major generation/load centers. The voltage samples are taken immediately after a fault is cleared and used as inputs to a binary classifier based on support vector machines to iden-tify the transient stability condition. The classifier is trained using examples of the post-fault recovery voltages (inputs) obtained through simulations and the corresponding sta-bility status (output) determined using a power angle-based stability index. Studies with the New England 39-bus test system indicate that the proposed algorithm can correctly recognize when the power system is approaching transient instability. The proposed sys-tem is then applied to Venezuelan power system and Manitoba Hydro power grid to demonstrate the applicability for large practical power systems. Performance of the pro-posed transient stability prediction scheme under the presence of asymmetrical faults, voltage sensitive loads, unlearned network topologies and measurement noise was found to be satisfactory. Once an impending transient instability situation has been detected, appropriate emer-gency control strategies are triggered to minimize the impact of this on the safe operation of the network and reduce the possibility of a blackout. This thesis examines two differ-ent emergency control schemes: a) A fuzzy logic based emergency load and generator shedding scheme and b) A high voltage direct current (HVdc) power order reduction scheme based on synchronized phasors measurements. These strategies were developed for two power systems with contrasting characteristics: one for the Venezuelan power system which is a conventional power system completely based on alternating current (AC) transmission, and the other for the Manitoba Hydro network which heavily depend on long HVdc transmission for power transfer. The proposed wide area control systems demonstrated good performance on the Venezuelan and Manitoba Hydro power grids.

Thesis (Ph. D.)--University of Adelaide, 1992. / Includes bibliographical references (leaves 329-349).

The presence of inter-area oscillations has long affected stability constraints, and therefore, limited the power transfer capacity of interconnected power systems. Adequate damping of these inter-area oscillations is, thus, necessary to secure system operation and ensure system reliability while increasing power transfers. Power system stabilizers (PSS) are the most common devices used to enhance the damping of such oscillations. Many studies have demonstrated that PSSs using remote signals may perform better than using local signals. The advent of phasor measurement units (PMU) makes remote or wide-area signals become available, which enables various important applications. Of particular interest is wide-area damping control (WADC), which aims to utilize remote or wide-area measurements to damp the inter-area oscillations. However, two main challenges in WADC design are (1) feedback controller input signal selection (which PMU signal is best to use?), and (2) latency (which is inherent in the transmission of the measurements) considerations. In response to the first challenge, this thesis proposes a concept called dominant inter-area oscillation path, which serves to pinpoint a set of candidate signals that can be used as the feedback controller inputs by locating the interconnected corridors where the inter-area modal contents are the most observable. Derivation, identification, and use of the dominant inter-area oscillation paths are demonstrated throughout the thesis. Extensive analysis on the relationships between the proposed set of signals and system properties regarding stability and robustness is presented. To tackle the second challenge, the impacts of time delays on the system performance when using the dominant path signals are investigated. To date, several studies have proposed different control design methods using various oscillation dampers to design WADC. Nevertheless, neither a systematic method nor a concept that encompasses fundamental knowledge on power system dynamics has yet been offered. The objective of this thesis is, thus, to propose an analytical framework based on the dominant path concept which is built upon fundamental principles for feedback controller input signal selection in WADC. With this framework, a proper and systematic approach is developed. The proposed method allows to select appropriate signals and use them to effectively mitigate the inter-area oscillations that constrain power transfer capacity and affect system stability. / <p>QC 20150414</p>

The study of nonlinear dynamical systems via the spectrum of the Koopman operator has emerged as a paradigm shift, from the Poincaré's geometric picture that centers the attention on the evolution of states, to the Koopman operator's picture that focuses on the evolution of observables. The Koopman operator-theoretic framework rests on the idea of lifting the states of a nonlinear dynamical system to a higher dimensional space; these lifted states are referred to as the Koopman eigenfunctions. To determine the Koopman eigenfunctions, one performs a nonlinear transformation of the states by relying on the so-called observables, that is, scalar-valued functions of the states. In other words, one executes a change of coordinates from the state space to another set of coordinates, which are denominated Koopman canonical coordinates. The variables defined on these intrinsic coordinates will evolve linearly in time, despite the underlying system being nonlinear. Since the Koopman operator is linear, it is natural to exploit its spectral properties. In fact, the theory surrounding the spectral properties of linear operators has well-known implications in electric power systems. Examples include small-signal stability analysis and direct methods for transient stability analysis based on the Lyapunov function. From the applications' standpoint, this framework based on the Koopman operator is attractive because it is capable of revealing linear and nonlinear modes by only applying well-established tools that have been developed for linear systems. With the challenges associated with the high-dimensionality and increasing uncertainties in the power systems models, researchers and practitioners are seeking alternative modeling approaches capable of incorporating information from measurements. This is fueled by an increasing amount of data made available by the wide-scale deployment of measuring devices such as phasor measurement units and smart meters. Along these lines, the Koopman operator theory is a promising framework for the integration of data analysis into our mathematical knowledge and is bringing an exciting perspective to the community. The present dissertation reports on the application of the Koopman operator for identification, estimation, and control of electric power systems. A dynamic state estimator based on the Koopman operator has been developed and compares favorably against model-based approaches, in particular for centralized dynamic state estimation. Also, a data-driven method to compute participation factors for nonlinear systems based on Koopman mode decomposition has been developed; it generalizes the original definition of participation factors under certain conditions. / PHD / Electric power systems are complex, large-scale, and given the bidirectional causality between economic growth and electricity consumption, they are constantly being expanded. In the U.S., some of the electric power grid facilities date back to the 1880s, and this aging system is operating at its capacity limits. In addition, the international pressure for sustainability is driving an unprecedented deployment of renewable energy sources into the grid. Unlike the case of other primary sources of electric energy such as coal and nuclear, the electricity generated from renewable energy sources is strongly influenced by the weather conditions, which are very challenging to forecast even for short periods of time. Within this context, the mathematical models that have aided engineers to design and operate electric power grids over the past decades are falling short when uncertainties are incorporated to the models of such high-dimensional systems. Consequently, researchers are investigating alternative data-driven approaches. This is not only motivated by the need to overcome the above challenges, but it is also fueled by the increasing amount of data produced by today’s powerful computational resources and experimental apparatus. In power systems, a massive amount of data will be available thanks to the deployment of measuring devices called phasor measurement units. Along these lines, the Koopman operator theory is a promising framework for the integration of data analysis into our mathematical knowledge, and is bringing an exciting perspective on the treatment of high-dimensional systems that lie in the forefront of science and technology. In the research work reported in this dissertation, the Koopman operator theory has been exploited to seek for solutions to some of the challenges that are threatening the safe, reliable, and efficient operation of electric power systems.