In industrial fuel gas preparation, there are several compositional properties that must be controlled within specified limits. This allows client plants to use the fuel gas mixture safely without having to adjust and control the composition themselves. The variables to be controlled are the Higher Heating Value (HHV), Wobbe Index (WI), Flame Speed Index (FSI), and Pressure (P). These variables are controlled by adjusting the volumetric flow rates of several inlet gas streams of which some are makeup streams (always available) and some are wild streams that vary in composition and availability (by-products of plants). The inlet streams need to be adjusted in the correct ratios to keep all the controlled variables (CVs) within limits while minimising the cost of the gas blend. Furthermore, the controller needs to compensate for fluctuations in inlet stream compositions and total fuel gas demand (the total discharge from the header). This dissertation describes the modelling and model validation of an industrial fuel gas header as well as a simulation study of three different Model Predictive Control (MPC) strategies for controlling the system while minimising the overall operating cost. / Dissertation (MEng)--University of Pretoria, 2011. / Electrical, Electronic and Computer Engineering / unrestricted

Inverters play key roles in connecting sustainable energy (SE) sources to the local loads and the ac grid. Although there has been a rapid expansion in the use of renewable sources in recent years, fundamental research, on the design of inverters that are specialized for use in these systems, is still needed. Recent advances in power electronics have led to proposing new topologies and switching patterns for single-stage power conversion, which are appropriate for SE sources and energy storage devices. The current source inverter (CSI) topology, along with a newly proposed switching pattern, is capable of converting the low dc voltage to the line ac in only one stage. Simple implementation and high reliability, together with the potential advantages of higher efficiency and lower cost, turns the so-called, single-stage boost inverter (SSBI), into a viable competitor to the existing SE-based power conversion technologies. The dynamic model is one of the most essential requirements for performance analysis and control design of any engineering system. Thus, in order to have satisfactory operation, it is necessary to derive a dynamic model for the SSBI system. However, because of the switching behavior and nonlinear elements involved, analysis of the SSBI is a complicated task. This research applies the state-space averaging technique to the SSBI to develop the state-space-averaged model of the SSBI under stand-alone and grid-connected modes of operation. Then, a small-signal model is derived by means of the perturbation and linearization method. An experimental hardware set-up, including a laboratory-scaled prototype SSBI, is built and the validity of the obtained models is verified through simulation and experiments. Finally, an eigenvalue sensitivity analysis is performed to investigate the stability and dynamic behavior of the SSBI system over a typical range of operation.

We examine the related nature of two identification algorithms, subspace identification (SID) and Dynamic Mode Decomposition (DMD), and their correctness properties over a broad range of problems. This investigation begins by noting the strong relationship between the two algorithms, both drawing significantly on the pseudoinverse calculation using singular value decomposition, and ultimately revealing that DMD can be viewed as a substep of SID. We then perform extensive computational studies, characterizing the performance of SID on problems of various model orders and noise levels. Specifically, we generate 10,000 random systems for each model order and noise level, calculating the average identification error for each case, and then repeat the entire experiment to ensure the results are, in fact, consistent. The results both quantify the intrinsic algorithmic error at zero-noise, monotonically increasing with model complexity, as well as demonstrate an asymptotically linear degradation to noise intensity, at least for the range under study. Finally, we close by demonstrating DMD's ability to recover system matrices, because its access to full state measurements makes them identifiable. SID, on the other hand, can't possibly hope to recover the original system matrices, due to their fundamental unidentifiability from input-output data. This is true even when SID delivers excellent performance identifying a correct set of equivalent system matrices.

Our data consist of measurements of 22 sharks' movements within a 366-acre tidal basin. The measurements are made at irregular time points over a 16-month interval. Constant-length observation intervals would have been desirable, but are often infeasible in practice. We model the sharks' paths at short constant-length intervals by inferring their behavior (feeding vs transiting), interpolating their locations, and estimating parameters of motion (speed and turning angle) in environmental and ecological contexts. We are interested in inferring regional differences in the sharks' behavior, and behavioral interaction between them. Our method uses particle filters, a computational Bayesian technique designed to sequentially model a dynamic system. We discuss how resampling is used to approximate arbitrary densities, and illustrate its use in a simple example of a particle filter implementation of a state-space model. We then introduce a particular model formulation that uses conditioning to introduce unobserved parameters for the shark's behaviors. We show how the irregularly-observed shark locations can be modeled by interpolation as a set of movements at constant-length time intervals. We use a spline method for generating approximations of the ground truth at these intervals for comparison with our model. Finally, we demonstrate our model's estimates of the sharks' behavioral and ecological parameters of interest on a subset of the observed data. / Statistics

The advent of synchronized wide-area frequency measurements obtained from frequency disturbance recorders and phasor measurement units has presented the power industry with special opportunities to study power system dynamics. I propose the use of wide-area frequency measurements in identifying system disturbances based on power system post-event modal properties. In this work, power system dynamics are examined from an internal system energy viewpoint. Since an electric power system is composed of coupled rotating machines (large generators) which have air gap magnetic fields that are essentially static, or quasi-static, the power system may be modeled as a system with energy stored in quasi-static magnetic fields. The magnetic fields in the machines do change with time but may be modeled as static as far as wave propagation is concerned. The dynamic model that I develop treats this magnetic energy specifically as potential energy. Each rotating machine also contains an inertia due to the mass and motion of its rotor train and so each machine contains a rotational kinetic energy. Thus the internal system energy for a power system dynamic model may be considered to be contained in potential (magnetic) and kinetic (rotating mass) energies. This notion of internal energy lends itself to the use of a state-space model where each system state is associated with either a kinetic energy or a potential energy. An n-machine system would have a total of 2n states and would thus be a 2n-th order system. For many power system disturbances, I postulate that a linearized version of this model may be used to examine system natural response in terms of frequency and phasor measurements. The disturbances that I will investigate include generator and line outages. For any particular outage, the power system exhibits a very specific natural response in terms of its kinetic and potential energies. Kinetic energy in the system is directly related to each specific machine's rotational speed. I propose that the kinetic energy corresponds directly with bus frequencies through a linear transformation. Likewise magnetic field energy in each machine corresponds directly with a torque angle. The potential energy in the system thus corresponds directly with bus angles through a linear transformation. The primary focus of this work is on frequency deviation modal characteristics – specifically damped oscillation frequencies, mode shapes, and damping ratios. This work presents how specific disturbances on a power system will lead to specific oscillation frequencies in the deviation quantities and that these oscillation frequencies may be used to identify the disturbance. The idea of disturbance identification stems out of previous work done in locating disturbances by using a distributed parameter (DP) model of an electric power system. This DP model, which assumes a wave-like motion of frequency and phase quantities, was used to locate disturbances via a triangulation method. This present work, instead of using a DP model of the power system, assumes lumped parameters and focuses on disturbance identification strictly via modal characteristics – particularly oscillation frequency in the frequency deviations. This model is not concerned with geographic location but focuses on system topology, loading, and machine mass as lumped parameters. Advantages of disturbance identification include mainly reliability enhancements but can also be used in marketing applications. The state-space model used to realize this theory is verified via simulation using small, "academic" systems which should prove useful in classroom settings. Additionally the model is verified on a larger test system in order prove its validity and potential usefulness on large power systems. / Ph. D.

The concept of piezohydraulic actuation is to transfer the reciprocal small stroke displacement of piezoceramics into unidirectional motion by frequency rectification through a hydraulic fluid. It takes advantage of the high force capabilities that piezoelectric materials have and couples it with very stiff media such as hydraulic fluid to amplify and create this unidirectional motion. Inlet and outlet valves are connected to a pumping chamber where pressure is built by the displacement of the piezoelectric material and released by the opening of the outlet valve, thus achieving a variable flow rate that is used to push a hydraulic cylinder. Loads may be connected to this hydraulic cylinder for measuring/achieving mechanical power. As part of this research, a benchtop piezohydraulic actuator with active piezohydraulic valves has been developed and the concept of piezohydraulic actuation has been demonstrated. Displacement of a hydraulic cylinder by driving a piezoelectric stack has been achieved while the cylinder was loaded or unloaded. Lumped-parameter state-space models have been developed in order to simulate the dynamics of the active valves and entire actuator system. The model simulates the chamber pressure, displacement of the hydraulic cylinder, and power of the piezohydraulic unit. A four-stage cycle simulation was used to model the pumping operation and dynamic response of the system. Experimental results demonstrate the importance of fluid compressibility, valve timing, and fluid circuit components in the optimization of the output power of the actuation system. An array of different timing tests run on the inlet and outlet valves shows that their timing is crucial to the performance of the system. Also shown is that the optimal timing conditions change slightly while under different loads. When operating at higher frequencies (above 140 Hz), it is shown that the hydraulic fluid circuit does not respond quickly enough for the piston to fully extend against the fluid and loaded cylinder. There is not sufficient time when operating at higher frequencies to push all the fluid from the chamber into the hydraulic cylinder, operation is too fast for the dynamics of the fluid circuit. The four stage lumped-parameter model achieves good approximations of the experimental results when the load inertia was neglected while operating at frequencies below 120 Hz and under loads at or below 12.825 kg. Memory limitations caused the number of elements included in the lumped-parameter model to be limited, and are believed to be the source of the errors for the higher operation frequencies and loads. The model never converged due to the lack of elements, and the simulated system did not respond quickly enough to accurately model the fluid exiting the chamber. When operating at frequencies above the 120 Hz value, this error in modeling the fluid exiting the valves becomes very important. The simulation predicts higher values than the experiment and fails to correlate to the actual results at the higher frequencies and while under the higher loads. The errors at higher loads may also be attributed to the neglected inertia. The most recent tests on the benchtop set-up were all run with a pre-pressure value of 190 psi, a piston duty cycle of 50%, valve duty cycles of 40% for each, and a 5% outlet valve offset. Slightly better operation performance might be achieved at frequencies higher than 140 Hz by increasing the piston duty cycle and varying the valve parameters. Also, increaing the pre-pressure of the fluid may help by stiffening the system to create a faster response, however this will have an adverse effect also by creating more force against piston motion. Lastly, the hydraulic cylinder was built for high pressures and had considerable friction associated with it. Obtaining a different cylinder with less friction may also help the response time of the fluid circuit. / Master of Science

Avian populations have undergone global declines that have profound implications for biodiversity. The prognosis of avian decline risks has been hindered by a lack of understanding of the endogenous and exogenous determinants of avian fauna declines. I investigated the spatiotemporal population dynamics of 428 North American breeding birds using the Breeding Bird Survey data from 1970 to 2018. I hypothesized life history strategies would determine avian population trends by mediating population regulation and responses to global climate changes (H1). I also hypothesized birds with increasing or stable population trends would have greater within-species spatial variability in their population responses to local climate changes and abundances than species with decreasing trends (H2). Machine learning methods classified 225 species (53%) to a decreasing group and 203 species (47%) to an increasing group. The effects of North Atlantic Oscillation (NAO) and Southern Oscillation (SO) on continentally aggregated populations were significantly greater in the increasing group than the decreasing group. However, neither direct nor delayed density dependence differed between the two groups. Bayesian phylogenetic logistic regression demonstrated that increased fledging age significantly reduced avian population decline risks, suggesting that increased investments of parental care mitigate avian population decline risks. Birds living in open areas had about 50% higher risks of population declines than those associated with densely vegetated ecosystems, signaling alarming avian faunal decline risks caused by converting grasslands and shrublands to agriculture or other land use. Structural equation models demonstrated that life history strategy was a direct causal factor of density dependence and population responses to NAO and SO and an indirect cause of avian population decline via mediating avian responses to SO, supporting H1. In metapopulations of 159 breeding birds from 1985 to 2018, density dependence did not differ significantly between the decreasing and increasing groups; however, bird species in the increasing group had greater within-species spatial variance in population responses to temperature and precipitation than declining species, partially supporting H2. Global changes may homogenize avian life history traits and population responses to climate changes, which in turn increase avian fauna decline risks.