Ce travail de thèse a pour finalité de présenter différentes méthodologies pour l’analyse de réseaux à courant continue haut tension (HVDC). La théorie fondamentale de l‘analyse de sensitivité s’applique sur l’analyse des systèmes électriques de puissance dans les différentes étapes de la production et la transmission de l’énergie. Des outils de bases concernant cette théorie sont devenus populaires et très utilisés.En pratique, les facteurs de participation représentent un exemple de ces outils, utilisés depuis longtemps. Ils ont été proposés par (Perez-Arriaga et al., 1982). Le but des facteurs de participation est la réduction des modèles des systèmes ayant une dynamique particulière comme celle des systèmes électromécaniques. Aussi, les facteurs de participation sont utilisés pour comprendre les relations entre les variables d’état et les valeurs propres des systèmes. Nous présentons la théorie fondamentale de la sensitivité analytique dans laquelle les facteurs de participation sont basses. Pour une bonne compréhension de ces éléments de base de cette théorie, quelques exemples sont présentés. Nous présentons une formulation différente pour l’analyse par sensitivité paramétrique (Barrera Gallegos et al., 2016).Enfin, les deux méthodologies sur des exemples des réseaux HVDC. Notre comparaison permet d’exposer les limites de l’utilisation des facteurs de participation sur des réseaux HVDC.En conclusion, la nouvelle méthodologie est plus générale comparée aux facteurs de participation. Egalement, la nouvelle méthodologie, par sensitivité paramétrique, nous donne plus d’informations sur les caractéristiques dynamiques des réseaux HVDC. / The work presented in this thesis presents different methodology for parametric sensitivity of high voltage dc networks(HVDC).The fundamental theory of modal analysis has been applied for analysis of the power electrical systems in its different stages of production and transmission of energy. Tools derived from these fundamentals have become popular with its use. Among the tools used in dynamic analysis, participation factors have been used for a long time. Proposed by (Perez-Arriaga et al., 1982), they give a metric for relating states and eigenvalues of a system. The objective of the participation factors is to analyze systems with particular dynamics such as electromechanical systems. The participation factors is a tool that helps in the reduction of systems. Firstly, we present the fundaments of the sensitivity analysis upon which the participation factors are based on. The principle is illustrated with several examples.We propose a new formulation for sensitivity analysis using parametric sensitivity (Barrera Gallegos et al., 2016).In the latter, the application of participation factors and parametric sensitivity analysis is performed using HVDC networks. This comparison exposes the limitation of the participation factors for the general analysis of HVDC grids.In conclusion, the new methodology is a better and general alternative compared to traditional participation factors employed for analysis of HVDC grids. In addition, the new technique of parametric sensitivity produces several novel information related to the dynamic characteristics of the HVDC grid.

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.

The average South African tertiary student spends more than the average South African individual each month, making them a valuable market to be understood by retailers. One technique retailers use to differentiate themselves from the increased competition is by relationship marketing through loyalty programmes. Research on the topic of loyalty programmes increased rapidly over the last decade. The problem, however, is that the majority of past research on the subject of loyalty programmes only focused on what happens after customers have already joined a loyalty programme. Little research exists regarding the factors that influence subject participation in loyalty programmes. Furthermore, to date no research study has investigated factors that might influence loyalty programme participation among South African students. The purpose of this study was to understand the tertiary student market by building on the research of De Wulf et al. (2013:69-83) as a vital study to explore factors that might influence them to participate in retail loyalty programmes. An exploratory study was conducted, in which data was collected from students studying at Pearson Institute of Higher Education/ CTI Pretoria by means of self-administered questionnaires. The study followed a quantitative research approach, in order to satisfy the research objectives. The results of the study indicate that the top four characteristics most likely to influence students to participate in a retail loyalty programme, include: a loyalty programme where you receive immediate discounts on certain items at purchase; a loyalty programme that allows you to use your loyalty card at more than one retailer; a loyalty programme with unlimited duration on the usage of benefits or rewards; a loyalty programme that rewards you by giving immediate benefits. / Business Management / M. Com. (Business Management)

1. Einführung in die Theorie dynamischer Analysen mit Creo Simulate 2. Modalanalysen (Standard und mit Vorspannung) 3. Dynamische Analysen einschließlich Klassifizierung der Analysen; einige einfache Beispiele für eigene Studien (eine Welle unter Unwuchtanregung und ein Ein-Massen-Schwinger) sowie etliche Beispiele größerer dynamischer Systemmodelle aus unterschiedlichsten Anwendungsbereichen 4. Feedback an den Softwareentwickler PTC (Verbesserungsvorschläge und Softwarefehler) 5. Referenzen / 1. Introduction to dynamic analysis theory in Creo Simulate 2. Modal analysis (standard and with prestress) 3. Dynamic analysis, including analysis classification, some simple examples for own self-studies (shaft under unbalance excitation and a one-mass-oscillator) and several real-world examples of bigger dynamic systems 4. Feedback to the software developer PTC (enhancement requests and code issues) 5. References

1. Einführung in die Theorie dynamischer Analysen mit Creo Simulate 2. Modalanalysen (Standard und mit Vorspannung) 3. Dynamische Analysen einschließlich Klassifizierung der Analysen; einige einfache Beispiele für eigene Studien (eine Welle unter Unwuchtanregung und ein Ein-Massen-Schwinger) sowie etliche Beispiele größerer dynamischer Systemmodelle aus unterschiedlichsten Anwendungsbereichen 4. Feedback an den Softwareentwickler PTC (Verbesserungsvorschläge und Softwarefehler) 5. Referenzen / 1. Introduction to dynamic analysis theory in Creo Simulate 2. Modal analysis (standard and with prestress) 3. Dynamic analysis, including analysis classification, some simple examples for own self-studies (shaft under unbalance excitation and a one-mass-oscillator) and several real-world examples of bigger dynamic systems 4. Feedback to the software developer PTC (enhancement requests and code issues) 5. References