Research in sustainable energy sources has become the interest of many studies due to the increasing energy demand and the amount of wasted energy released from existing methods, along with their effect on climate change and environment sustainability. Thermo-Electric Generators (TEGs) are a potential solution that is being studied and implemented as they can convert low grade thermal energy to useful electrical energy at various operating conditions. The integration of a TEG within a heat exchanger (TEG/HX) system connected to an electrical DC micro-grid, using a Maximum Power Point Tracking (MPPT) system is the focus of this study. Using a numerical TEG/HX model from a previous study and a developed DC micro-grid model the interaction between the thermal and electrical aspects were investigated with the focus on the electrical performance of the system. The main concern of this study is to investigate the effect of the sub components of the DC micro-grid on the overall available energy. An analytic model was developed to estimate the power loss in the electrical circuit of the micro-grid, the model utilizes the equations for switching and conduction losses which have been used by several studies. Other variables such as the battery characteristics and electrical load profiles were also investigated by simulating several case studies including changing operating conditions. This study shows the effect of a TEG configuration on the power loss in an electrical system using power loss curves in comparison with the Open Circuit Voltage (OCV) of such configuration. It also covers important modes of operation for the battery, loads and MPPT for a stable and reliable operation of an isolated DC micro-grid system were TEGs are the only source of power. The result of the study presented is a system design that is able to maximize the electrical energy harvested from the TEGs to extend the operation of the dc-micro-grid first by applying a suitable TEG configuration and consequently a suitable electrical circuit. Secondly, by adapting to the changing operating conditions of the TEGs and the loads; and compensating for these changes using the battery storage system. / Thesis / Master of Applied Science (MASc)

The goal of this thesis is to develop nontraditional strategies to provide motion control for different engineering applications. We focus our attention on three topics: 1) roll reduction of ships in a seaway; 2) response reduction of buildings under seismic excitations; 3) new training strategies and neural-network configurations. The first topic of this research is based on a multidisciplinary simulation, which includes ship-motion simulation by means of a numerical model called LAMP, the modeling of fins and computation of the hydrodynamic forces produced by them, and a neural-network/fuzzy-logic controller. LAMP is based on a source-panel method to model the flowfield around the ship, whereas the fins are modeled by a general unsteady vortex-lattice method. The ship is considered to be a rigid body and the complete equations of motion are integrated numerically in the time domain. The motion of the ship and the complete flowfield are calculated simultaneously and interactively. The neural-network/fuzzy-logic controller can be progressively trained. The second topic is the development of a neural-network-based approach for the control of seismic structural response. To this end, a two-dimensional linear model and a hysteretic model of a multistory building are used. To control the response of the structure a tuned mass damper is located on the roof of the building. Such devices provide a good passive reduction. Once the mass damper is properly tuned, active control is added to improve the already efficient passive controller. This is achieved by means of a neural network. As part of the last topic, two new flexible and expeditious training strategies are developed to train the neural-network and fuzzy-logic controllers for both naval and civil engineering applications. The first strategy is based on a load-matching procedure, which seeks to adjust the controller in order to counteract the loads (forces and moments) which generate the motion that is to be reduced. A second training strategy provides training by means of an adaptive gradient search. This technique provides a wide flexibility in defining the parameters to be optimized. Also a novel neural-network approach called modal neural network is designed as a suitable controller for multiple-input multiple output control systems (MIMO). / Ph. D.