The advent of Wide-Area measurement Systems has spurred interest in the use of non-local feedback signals for power swing damping control. Although damping can be improved through generator excitation systems, dc links and other grid connected power electronic converters, the full potential of wide-area measurements can be realized by coordinating the strategies used for multiple controllable devices in a grid. These strategies also need to be robust to partial or complete loss of communication, changes in operating points, topology and equipment outages, improve damping of all the controllable swing modes, and have adequate stability margins to avoid destabilization of untargeted modes. This thesis investigates a control strategy for multi-infeed and multi-terminal (also referred to as multiple embedded dc links in this thesis) dc links using local frequency difference signals as well as the frequency difference signals obtained from other dc links. This strategy combines the advantages of the local frequency difference signal with the additional degrees of freedom provided by the use of non-local frequency difference signals, to achieve targeted and enhanced swing mode damping for the poorly damped modes. Since the strategy uses only a limited set of non-local signals, the signals may be directly communicated to the dc links without having to be centrally collated with other system-wide measurements. The key aspect of the proposed strategy is the use of a symmetric positive definite (spd) gain matrix. This results in enhanced damping for all controllable swing modes. Furthermore, loss of communication between the dc links does not destroy the symmetric positive definiteness and the gain elements can be tuned to selectively enhance damping of poorly damped modes. Eigenvalue sensitivity analysis and case studies on a 3 machine 9 bus and 16 machine 68 bus system with multiple HVDC links are presented to demonstrate the key attributes and the effectiveness of this strategy. / October 2016

Student Number : 8609118F - MSc(Eng) project report - School of Electrical Engineering - Faculty of Engineering and the Built Environment / Technical support organisations (TSOs) and service groups are becoming increasingly important in the upkeep and maintenance of the highly sophisticated and optimised automation equipment and systems that drive production in the mining and manufacturing industries. This study presents a methodology for designing and analysing the performance of a TSO, with a view to ongoing optimisation. The method of approach is the ‘System Dynamics’ (SD) simulation technique, which has at its core a mathematical model of the organisational unit, and which attempts to model its behaviour over time. The organisational design is based on Integrated Logistics Support (ILS) principles. It was found that the System Dynamics modelling approach was useful for a technical support organisation. For managers and organisational strategists, the quantitative ability of SD provides an intuitive and practical way to evaluate the impact of different structural and operational scenarios in response to ever-demanding business imperatives.

No / Risk assessment is a strategic approach for building resilient supply chains in different types of industries, including the energy sector. In case of an important event with a high level of the risk happening, it could jeopardize the operations of an organization. This could even interrupt the delivery of a product or service, damaging the profitability and reputation of the organization. This could also have a long-term effect on the strategic performance of the firm. Furthermore, key risks might be linked through causal interrelationships. Given the nature of a dynamic business environment, risk assessment also needs to consider the effect of time on those risks. Hence, it is important to understand the behaviour of a complex and dynamic system of interrelated risks to improve strategic decision making in the energy sector. This paper presents a System Dynamics model to assess risks with a supply chain perspective for power plants. The risk assessment model was validated with a case study of three power plants in the Middle East. The developed risk assessment model allowed to understand the long-term effect of risk on three performance indicators: availability of the power plant, efficiency, and operational and maintenance costs. The model provides a different approach to traditional risk assessment by proposing a dynamic modelling methodology.

<p>In this thesis, an explicit time integration code which integrates multibody dynamics (MBD) and the discrete element method (DEM) is validated using three previously published steady-state physical experiments for non-cohesive sand-type material, namely: shear-cell for measuring shear stress versus normal stress; penetroplate pressure-sinkage test; and wheel drawbar pull-torque-slip test. The test results are used to calibrate the material properties of the DEM soft soil model and validate the coupled MBD-DEM code. All three tests are important because each test measures specific mechanical characteristics of the soil under various loading conditions. Shear strength of the soil as a function of normal load help to understand shearing of the soil under a vehicle wheel contact patch causing loss of traction. Penetroplate pressure-sinkage test is used to calibrate and validate friction and shear strength characteristics of the soil. Finally the rigid wheel-soil interaction test is used to predict drawbar pull force and wheel torque vs. slip percentage and normal stress for a rigid wheel. Wheel-Soil interaction test is important because it plays the role of ultimate validation of the soil model tuned in the previous two experiments and also shows how the soil model behaves in vehicle mobility applications.</p> <p><br></p> <p>All the aforementioned tests were modeled in the multibody dynamics software using rigid bodies and various joints and actuators. The sand-type material is modeled using discrete cubical particles. A penalty technique is used to impose normal contact constraints (including particle-particle and particle-wall contact). An asperity-based friction model is used to model friction. A Cartesian Eulerian grid contact search algorithm is used to allow fast contact detection between particles. A recursive bounding box contact search algorithm enabled fast contact detection between the particles and polygonal body surfaces (such as walls, penetrometer, and wheel). The governing equations of motion are solved along with contact constraint equations using a time-accurate explicit solution procedure. The results show very good agreement between the simulation and the experimental measurements. The model is then demonstrated in a full-scale application of high-speed off-road vehicle mobility on the sand-type soil.</p>

This report describes MM, a computer program that can model a variety of mechanical and fluid systems. Given a system's structure and qualitative behavior, MM searches for models using an energy-based modeling framework. MM uses general facts about physical systems to relate behavioral and model properties. These facts enable a more focussed search for models than would be obtained by mere comparison of desired and predicted behaviors. When these facts do not apply, MM uses behavior-constrained qualitative simulation to verify candidate models efficiently. MM can also design experiments to distinguish among multiple candidate models.

A spatial-temporal model is developed for modelling the impacts of simulated coastal zone storm surge and flooding using a combined spatial mapping and system dynamics approach. By coupling geographic information systems (GIS) and system dynamics, the interconnecting components of the spatial-temporal model are used with limited historical data to evaluate storm damage. Overlapping cumulative effects layers in GIS (ArcMap) are used for describing the coastal community’s profile, and a system dynamics feedback model (STELLA) is developed to define the interconnecting component relationships of the community. The component-wise changes to the physical environment, community infrastructure, and socioeconomic resources from the storm surge and seal level rise are examined. These changes are used to assess the impacts of the community system as a whole. For the purpose of illustrating this model, the research is applied specifically to the case of Charlottetown, Prince Edward Island, Canada, a vulnerable coastal city subject to considerable impacts from pending sea level rise and more frequent severe storm surge attributed to the changing climate in the coastal zone.

Oil sands mining results in significant disturbances to natural ecosystems when soil and overburden materials are removed and stockpiled to provide access to mined materials. The mining process must be followed by land reclamation, whereby disturbed landscapes are recovered with the intent to replicate the performance of natural watersheds. Modeling hydrological processes in reclaimed landscapes is essential to assess the hydrological performance of the reclamation strategies as well as their evolution over time, and requires a reliable and continuous source of input data. In pursuit of simulating the various hydrological processes, such as soil moisture and actual evapotranspiration, a lumped generic system dynamics watershed (GSDW) model has been developed. The validity of the proposed model has been assessed in terms of its capacity to reproduce the hydrological behaviour of both reconstructed and natural watersheds.<p> Data availability is a major challenge that constrains not only the type of models used but also their predictive ability and accuracy. This study evaluates the utility of precipitation and temperature data from the North American Regional Reanalysis (NARR) versus conventional platform data (e.g., meteorological station) for the hydrological modeling. Results indicate NARR data is a suitable alternative to local weather station data for simulating soil moisture patterns and evapotranspiration fluxes despite the high complexity involved in simulating such processes. Initially, the calibrated GSDW model was used along with available historical meteorological records, from both Environment Canada and NARR, to estimate the maximum soil moisture deficit and annual evapotranspiration fluxes. A probabilistic framework was adopted, and frequency curves of the maximum annual moisture deficit values were consequently constructed and used to assess the probability that various reconstructed and natural watersheds would provide the desired moisture demands. The study shows a tendency for the reconstructed watersheds to provide less moisture for evapotranspiration than natural systems. The probabilistic framework could be implemented to integrate information gained from mature natural watersheds (e.g., the natural system canopy) and transfer the results to newly reconstructed systems.<p> Finally, this study provided some insight into the sensitivity of soil moisture patterns and evapotranspiration to possible changes in the projected precipitation and air temperature in the 21st century. Climate scenarios were generated using daily, statistically downscaled precipitation and air temperature outputs from global climate models (CGCM3), under A2 and B1 emission scenarios, to simulate the corresponding soil moisture and evapotranspiration using the GSDW model. Study results suggest a decrease in the maximum annual moisture deficit will occur due to the expected increase in annual precipitation and air temperature patterns, whereas actual evapotranspiration and runoff are more likely to increase.

Oil sands mining results in significant disturbances to natural ecosystems when soil and overburden materials are removed and stockpiled to provide access to mined materials. The mining process must be followed by land reclamation, whereby disturbed landscapes are recovered with the intent to replicate the performance of natural watersheds. Modeling hydrological processes in reclaimed landscapes is essential to assess the hydrological performance of the reclamation strategies as well as their evolution over time, and requires a reliable and continuous source of input data. In pursuit of simulating the various hydrological processes, such as soil moisture and actual evapotranspiration, a lumped generic system dynamics watershed (GSDW) model has been developed. The validity of the proposed model has been assessed in terms of its capacity to reproduce the hydrological behaviour of both reconstructed and natural watersheds.<p> Data availability is a major challenge that constrains not only the type of models used but also their predictive ability and accuracy. This study evaluates the utility of precipitation and temperature data from the North American Regional Reanalysis (NARR) versus conventional platform data (e.g., meteorological station) for the hydrological modeling. Results indicate NARR data is a suitable alternative to local weather station data for simulating soil moisture patterns and evapotranspiration fluxes despite the high complexity involved in simulating such processes. Initially, the calibrated GSDW model was used along with available historical meteorological records, from both Environment Canada and NARR, to estimate the maximum soil moisture deficit and annual evapotranspiration fluxes. A probabilistic framework was adopted, and frequency curves of the maximum annual moisture deficit values were consequently constructed and used to assess the probability that various reconstructed and natural watersheds would provide the desired moisture demands. The study shows a tendency for the reconstructed watersheds to provide less moisture for evapotranspiration than natural systems. The probabilistic framework could be implemented to integrate information gained from mature natural watersheds (e.g., the natural system canopy) and transfer the results to newly reconstructed systems.<p> Finally, this study provided some insight into the sensitivity of soil moisture patterns and evapotranspiration to possible changes in the projected precipitation and air temperature in the 21st century. Climate scenarios were generated using daily, statistically downscaled precipitation and air temperature outputs from global climate models (CGCM3), under A2 and B1 emission scenarios, to simulate the corresponding soil moisture and evapotranspiration using the GSDW model. Study results suggest a decrease in the maximum annual moisture deficit will occur due to the expected increase in annual precipitation and air temperature patterns, whereas actual evapotranspiration and runoff are more likely to increase.

A spatial-temporal model is developed for modelling the impacts of simulated coastal zone storm surge and flooding using a combined spatial mapping and system dynamics approach. By coupling geographic information systems (GIS) and system dynamics, the interconnecting components of the spatial-temporal model are used with limited historical data to evaluate storm damage. Overlapping cumulative effects layers in GIS (ArcMap) are used for describing the coastal community’s profile, and a system dynamics feedback model (STELLA) is developed to define the interconnecting component relationships of the community. The component-wise changes to the physical environment, community infrastructure, and socioeconomic resources from the storm surge and seal level rise are examined. These changes are used to assess the impacts of the community system as a whole. For the purpose of illustrating this model, the research is applied specifically to the case of Charlottetown, Prince Edward Island, Canada, a vulnerable coastal city subject to considerable impacts from pending sea level rise and more frequent severe storm surge attributed to the changing climate in the coastal zone.

Heart disease, cancer, diabetes and other complex diseases account for more than half of human mortality in the United States. Other diseases such as AIDS, asthma, Parkinson’s disease, Alzheimer’s disease and cerebrovascular ailments such as stroke not only augment this mortality but also severely deteriorate the quality of human life experience. In spite of enormous financial support and global scientific effort over an extended period of time to combat the challenges posed by these ailments, we find ourselves short of sighting a cure or vaccine. It is widely believed that a major reason for this failure is the traditional reductionist approach adopted by the scientific community in the past. In recent times, however, the systems biology based research paradigm has gained significant favor in the research community especially in the field of complex diseases. One of the critical components of such a paradigm is computational systems biology which is largely driven by mathematical modeling and simulation of biochemical systems. The most common methods for simulating a biochemical system are either: a) continuous deterministic methods or b) discrete event stochastic methods. Although highly popular, none of them are suitable for simulating multi-scale models of biological systems that are ubiquitous in systems biology based research. In this work a novel method for simulating biochemical systems based on a deterministic solution is presented with a modification that also permits the incorporation of stochastic effects. This new method, through extensive validation, has been proven to possess the efficiency of a deterministic framework combined with the accuracy of a stochastic method. The new crossover method can not only handle the concentration and spatial gradients of multi-scale modeling but it does so in a computationally efficient manner. The development of such a method will undoubtedly aid the systems biology researchers by providing them with a tool to simulate multi-scale models of complex diseases.