34th Midwest Symposium on Circuits and Systems-Final Program

Organized by the Naval Postgraduate School Monterey California. Cosponsored by the IEEE Circuits and Systems Society. Symposium Organizing Committee: General Chairman-Sherif Michael, Technical Program-Roberto Cristi, Publications-Michael Soderstrand, Special Sessions- Charles W. Therrien, Publicity: Jeffrey Burl, Finance: Ralph Hippenstiel, and Local Arrangements: Barbara Cristi

Dynamic modeling of pwm and single-switch single-stage power factor correction converters

The concept of averaging has been used extensively in the modeling of power electronic circuits to overcome their inherent time-variant nature. Among various methods, the PWM switch modeling approach is most widely accepted in the study of closed-loop stability and transient response because of its accuracy and simplicity. However, a non-ideal PWM switch model considering conduction losses is not available except for converters operating in continuous conduction mode (CCM) and under small ripple conditions. Modeling of conductor losses under large ripple conditions has not been reported in the open literature, especially when the converter operates in discontinuous conduction mode (DCM). In this dissertation, new models are developed to include conduction losses in the non-ideal PWM switch model under CCM and DCM conditions. The developed model is verified through two converter examples and the effect of conduction losses on the steady state and dynamic responses of the converter is also studied. Another major constraint of the PWM switch modeling approach is that it heavily relies on finding the three-terminal PWM switch. This requirement severely limits its application in modeling single-switch single-stage power factor correction (PFC) converters, where more complex topological structures and switching actions are often encountered. In this work, we developed a new modeling approach which extends the PWM switch concept by identifying the charging and discharging voltages applied to the inductors. The new method can be easily applied to derive large-signal models for a large group of PFC converters and the procedure is elaborated through a specific example. Finally, analytical results regarding harmonic contents and power factors of various PWM converters in PFC applications are also presented here

Circuit paradigm in the 21

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Dynamic Modeling and Simulation of SAG Mill Circuits with Pebble Crushing

Grinding is one of the most energy-consuming processes in the mining industry. As a critical part of the comminution process, autogenous grinding (AG) or semi-autogenous grinding (SAG) mills are often used for primary grinding. However, the breakage mechanism of an AG/SAG mill is inefficient in grinding particles of a certain size, typically in the range of 25-55 mm, i.e., pebbles. Therefore, cone crushers are often used as pebble crushers and integrated into AG/SAG mill circuits to break the critical size particles that accumulate in the mill and to increase the performance of the primary grinding circuits.Many studies have been carried out, mainly focusing on optimizing of SAG mills and cone crushers, respectively, but only a few have investigated the dynamic interactions between a SAG mill and its pebble crushers. The scope of this thesis is to examine the dynamic relations between the SAG mill and the pebble crusher in a closed circuit and thus to optimize the circuit efficiency by controlling the pebble crusher operational settings.In this thesis, two modeling techniques are proposed for simulating the dynamics in the grinding process. The first method is the fundamental modeling method, where the underlying physics of the comminution process has been considered. The proposed mill model is divided into sub-processes that include breakage behavior in each sub-division, particle transportation within the mill chamber, and the discharge rate from the mill. The dynamic cone crusher model describes the crusher chamber as a surge bin and predicts the product particle sizes based on crusher CSS and eccentric speed. In the simulation model, other production units such as screens and conveyors are included to describe the dynamics of the circuit better. The flexibility of this method allows one to apply this simulation library to a variety of plants with different configurations.The second modeling technique presented in this study is based on data-driven methods, where two SAG mill power models are developed. The first model calculates the mill power draw by combining several individual data-driven algorithms. The second model uses historical data to forecast the mill power draw in advance. These data-driven methods can make high accuracy predictions based on a specific plant dataset, and find complex nonlinear relations between input variables and target outputs.The results from both simulations and industrial data analysis show that significant dynamic impact can be induced by altering the pebble crusher operational settings. Therefore, the performance (throughput or specific energy) of an AG/SAG closed circuit can be improved with the optimized utilization of its recycle pebble crusher. While the present work is based on simulation and analysis of plant data, full-scale tests and further model development are needed as part of a future study

Power Converters in Power Electronics

In recent years, power converters have played an important role in power electronics technology for different applications, such as renewable energy systems, electric vehicles, pulsed power generation, and biomedical sciences. Power converters, in the realm of power electronics, are becoming essential for generating electrical power energy in various ways. This Special Issue focuses on the development of novel power converter topologies in power electronics. The topics of interest include, but are not limited to: Z-source converters; multilevel power converter topologies; switched-capacitor-based power converters; power converters for battery management systems; power converters in wireless power transfer techniques; the reliability of power conversion systems; and modulation techniques for advanced power converters

Enhanced IVA for audio separation in highly reverberant environments

Blind Audio Source Separation (BASS), inspired by the "cocktail-party problem", has been a leading research application for blind source separation (BSS). This thesis concerns the enhancement of frequency domain convolutive blind source separation (FDCBSS) techniques for audio separation in highly reverberant room environments. Independent component analysis (ICA) is a higher order statistics (HOS) approach commonly used in the BSS framework. When applied to audio FDCBSS, ICA based methods suffer from the permutation problem across the frequency bins of each source. Independent vector analysis (IVA) is an FD-BSS algorithm that theoretically solves the permutation problem by using a multivariate source prior, where the sources are considered to be random vectors. The algorithm allows independence between multivariate source signals, and retains dependency between the source signals within each source vector. The source prior adopted to model the nonlinear dependency structure within the source vectors is crucial to the separation performance of the IVA algorithm. The focus of this thesis is on improving the separation performance of the IVA algorithm in the application of BASS. An alternative multivariate Student's t distribution is proposed as the source prior for the batch IVA algorithm. A Student's t probability density function can better model certain frequency domain speech signals due to its tail dependency property. Then, the nonlinear score function, for the IVA, is derived from the proposed source prior. A novel energy driven mixed super Gaussian and Student's t source prior is proposed for the IVA and FastIVA algorithms. The Student's t distribution, in the mixed source prior, can model the high amplitude data points whereas the super Gaussian distribution can model the lower amplitude information in the speech signals. The ratio of both distributions can be adjusted according to the energy of the observed mixtures to adapt for different types of speech signals. A particular multivariate generalized Gaussian distribution is adopted as the source prior for the online IVA algorithm. The nonlinear score function derived from this proposed source prior contains fourth order relationships between different frequency bins, which provides a more informative and stronger dependency structure and thereby improves the separation performance. An adaptive learning scheme is developed to improve the performance of the online IVA algorithm. The scheme adjusts the learning rate as a function of proximity to the target solutions. The scheme is also accompanied with a novel switched source prior technique taking the best performance properties of the super Gaussian source prior and the generalized Gaussian source prior as the algorithm converges. The methods and techniques, proposed in this thesis, are evaluated with real speech source signals in different simulated and real reverberant acoustic environments. A variety of measures are used within the evaluation criteria of the various algorithms. The experimental results demonstrate improved performance of the proposed methods and their robustness in a wide range of situations

Diagnostic Applications for Micro-Synchrophasor Measurements

This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems

Design and Control of Power Converters 2020

In this book, nine papers focusing on different fields of power electronics are gathered, all of which are in line with the present trends in research and industry. Given the generality of the Special Issue, the covered topics range from electrothermal models and losses models in semiconductors and magnetics to converters used in high-power applications. In this last case, the papers address specific problems such as the distortion due to zero-current detection or fault investigation using the fast Fourier transform, all being focused on analyzing the topologies of high-power high-density applications, such as the dual active bridge or the H-bridge multilevel inverter. All the papers provide enough insight in the analyzed issues to be used as the starting point of any research. Experimental or simulation results are presented to validate and help with the understanding of the proposed ideas. To summarize, this book will help the reader to solve specific problems in industrial equipment or to increase their knowledge in specific fields

Modeling and analysis of power processing systems: Feasibility investigation and formulation of a methodology

A review is given of future power processing systems planned for the next 20 years, and the state-of-the-art of power processing design modeling and analysis techniques used to optimize power processing systems. A methodology of modeling and analysis of power processing equipment and systems has been formulated to fulfill future tradeoff studies and optimization requirements. Computer techniques were applied to simulate power processor performance and to optimize the design of power processing equipment. A program plan to systematically develop and apply the tools for power processing systems modeling and analysis is presented so that meaningful results can be obtained each year to aid the power processing system engineer and power processing equipment circuit designers in their conceptual and detail design and analysis tasks