The increasing penetration of renewable energy resources within the Great Britain (GB) transmission system has created much greater variability of power flows within the transmission network. Consequently, modern transmission networks are presented with an ever increasing range of operating conditions. As a result, decision making in the Electricity National Control Centre (ENCC) of the GB electrical power transmission system is becoming more complex and control room actions are required for reducing timescales in the future so as to enable optimum operation of the system. To maximise utilisation of the electricity transmission system there is a requirement for fast transient and dynamic stability control. In this regard, GB electrical power transmissions system reinforcement using new technology, such as High Voltage Direct Current (HVDC) links and Thyristor-Controlled Series Compensation (TCSC), is planned to come into operation. The research aim of this PhD thesis is to fully investigate the effects of HVDC lines on power system small-disturbance stability in the presence of operational uncertainties. The main research outcome is the comprehensive probabilistic assessment of the stability improvements that can be achieved through the use of supplementary damping control when applied to HVDC systems. In this thesis, two control schemes for small-signal dynamic stability enhancement of an embedded HVDC link are proposed: Modal Linear Quadratic Gaussian (MLQG) controller and Model Predictive Controller (MPC). Following these studies, probabilistic methodologies are developed in order to test of the robustness of HVDC based damping controllers, which involves using classification techniques to identify possible mitigation options for power system operators. The Monte Carlo (MC) and Point Estimated Method (PEM) are developed in order to identify the statistical distributions of critical modes of a power system in the presence of uncertainties. In addition, eigenvalue sensitivity analysis is devised and demonstrated to ensure accurate results when the PEM is used with test systems. Finally, the concepts and techniques introduced in the thesis are combined to investigate robustness for the widely adopted MLQG controller and the recently introduced MPC, which are designed as the supplementary controls of an embedded HVDC link for damping inter-area oscillations. Power system controllers are designed using a linearised model of the system and tuned for a nominal operating point. The assumption is made that the system will be operating within an acceptable proximity range of its nominal operating condition and that the uncertainty created by changes within each operating point can possibly have an adverse effect on the controller’s performance.

Due to the rapid and prosperous development of information technology, data centers are widely used in every aspect of social life, such as industry, economy or even our daily life. This work considers the idea of developing a data-driven model based model predictive control (MPC) to regulate temperature for a class of single-rack data centers (DCs). An auto-regressive exogenous (ARX) model is identified for our DC system using partial least square (PLS) to predict the behavior of multi-inputs-single-output (MISO) thermal system. Then an MPC controller is designed to control the temperature inside IT rack based on the identified ARX model. Moreover, fuzzy c-means (FCM) is employed to cluster the measured data set. Based on the clustered data sets, PLS is adopted to identify multiple locally linear ARX models which will be combined by appropriate weights in order to capture the nonlinear behavior of the highly-nonlinear thermal system inside the IT rack. The effectiveness of the proposed method is illustrated through experiments on our single-rack DC and it is also compared with proportional-integral (PI) control. / Thesis / Master of Applied Science (MASc)

Le travail présenté dans ce mémoire de thèse porte sur la tolérance aux défauts dans le cas des systèmes linéaires. Les moyens de communication numériques sont utilisés dans le cadre de la mise en oeuvre d'une architecture de commande tolérante aux défauts pour des systèmes complexes. Une coopération entre les modules de commande/diagnostic assure la tolérance à certains types de défauts qui affectent le système. La commande des systèmes est traditionnellement réalisée à partir d'un calculateur central qui collecte l'ensemble des informations relevées sur le procédé, puis les traite pour élaborer un ensemble de commande qui est appliqué au procédé. Avec le développement des systèmes commandés en réseaux (Networked Control System) et des systèmes embarqués, l'architecture des systèmes s'oriente vers une distribution des algorithmes de commande et de diagnostic. On se propose d'aborder le problème de la conception des stratégies de distribution de diagnostic/commande et de coopération des tâches de commande entre les sous-contrôleurs associés à chaque sous-système qui composent le système complexe et de prendre en compte les défauts des actionneurs et de capteurs affectant les sous-systèmes. Il s'agit alors d'élaborer une stratégie de commande coopérative visant à compenser les effets des défauts affectant le système. Les commandes locales sont des commandes prédictives à base de modèle (MPC: Model Predictive Control). Une analyse de stabilité a été faite en prenant en considération la défaillance du réseau de communication. / The work presented in this memory of thesis focuses on fault tolerance in the case of linear systems. Digital communication tools are used in the context of the implementation of an architecture for fault tolerant control of complex systems. A cooperation between the control/diagnosis blocks ensures the tolerance to certain types of faults which affect the system. Control systems is traditionally carried out starting from a central computer that collects all information gathered on the process. Then, these information are treated in order to develop a set of command which is applied to the process. Thanks to the development of the Networked System Control and embedded systems, systems architecture is oriented towards a distributed control and diagnostic algorithms. One proposes to address the problem of designing distribution strategies for diagnosis/control and control tasks cooperation between sub-controllers associated at each subsystem comprising the complex system and to take into account the faults on the actuators and sensors that affect the subsystems. Then a cooperative control strategy is proposed. It aims at compensating the effects of the faults affecting the system. Local controls are based on Model Predictive Control (MPC). An analysis of stability was made taking into account the failure of the communication network

The interest in autonomous vehicles has never been higher and there are several components that need to function for a vehicle to be fully autonomous; one of which is the ability to perform a parking at the end of a mission. The objective of this thesis work is to develop and implement an automatic parking system (APS) for a heavy-duty vehicle (HDV). A delimitation in this thesis work is that the parking lot has a known structure and the HDV is a truck without any trailer and access to more computational power and sensors than today's commercial trucks. An automatic system for searching the parking lot has been developed which updates an occupancy grid map (OGM) based on measurements from GPS and LIDAR sensors mounted on the truck. Based on the OGM and the known structure of the parking lot, the state of the parking spots is determined and a path can be computed between the current and desired position. Based on a kinematic model of the HDV, a gain-scheduled linear quadratic (LQ) controller with feedforward action is developed. The controller's objective is to stabilize the lateral error dynamics of the system around a precomputed path. The LQ controller explicitly takes into account that there exist an input delay in the system. Due to minor complications with the precomputed path the LQ controller causes the steering wheel turn too rapidly which makes the backup driver nervous. To limit these rapid changes of the steering wheel a controller based on model predictive control (MPC) is developed with the goal of making the steering wheel behave more human-like. A constraint for maximum allowed changes of the controller output is added to the MPC formulation as well as physical restrictions and the resulting MPC controller is smoother and more human-like, but due to computational limitations the controller turns out less effective than desired. Development and testing of the two controllers are evaluated in three different environments of varying complexity; the simplest simulation environment contains a basic vehicle model and serves as a proof of concept environment, the second simulation environment uses a more realistic vehicle model and finally the controllers are evaluated on a full-scale HDV. Finally, system tests of the APS are performed and the HDV successfully parks with the LQ controller as well as the MPC controller. The concept of a self-parking HDV has been demonstrated even though more tuning and development needs to be done before the proposed APS can be used in a commercial HDV.