Browsing by Author "Mo-Yuen Chow, Committee Member"

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Analysis and Optimization of 1200V Silicon Carbide Bipolar Junction Transistor
(2008-10-02) Gao, Yan; Mo-Yuen Chow, Committee Member; Mesut E Baran, Committee Member; Alex Q. Huang, Committee Chair; Doug Barlage, Committee Member
Classification and Modeling of Internet Applications
(2004-02-26) Patel, Parita; Dr. H. Joel Trussell, Committee Member; Mo-Yuen Chow, Committee Member; Dr. Arne A Nilsson, Committee Chair
The classification of Internet traffic is an active research topic due to its applicability in the areas like differentiated services and network security. The introduction of voice, video and other real-time applications to the Internet has resulted in increasing demand for service differentiation and has triggered the need for change in traffic handling on the Internet. Traditionally, such a classification is done using the packet header field of 'port number,' which is a unique number associated with the application that generated the packet. In addition to adding complexity and extra computation in traffic handling, certain recent developments in networking techniques have rendered port numbers unreliable for this purpose. This motivates our scheme of classification that uses the distribution of packet sizes in a buffer or collected during a short time interval at a switch or a router. We demonstrate that the applications can be classified by these distributions. This 'implicit' classification builds a foundation for estimation and prediction of traffic mix, which is a long-term goal of this research project.
Control and Protection for Distribution Networks with Distributed Generators
(2004-09-15) El-Markabi, Ismail Mohamed Shaker; Michael Young, Committee Member; Mo-Yuen Chow, Committee Member; Mesut Baran, Committee Chair; Joel Trussell, Committee Member
The connection of distributed generators to distribution networks greatly influences the performance and stability of such networks. The purpose of the dissertation has been to investigate and attempt to resolve the impacts of connecting distributed generators to distribution networks. Two main problems were defined and studied, which are the effect of distributed generators on the feeder voltage regulation and on the feeder protection schemes. A central supervisory controller designed to regulate the feeder's voltage is presented, and the architecture of the controller is shown along with test results illustrating the ability of the controller to regulate the voltage along a feeder with distributed generators with minimum cost. In an attempt to regulate the feeder's voltage without the need for the feeder's explicit model, a multi-agent based distributed controller is proposed. The multi-agent system structure and design are illustrated, and test results comparing the performance of the two proposed controllers are shown. The dissertation then presents an approach to extend conventional fault analysis studies to include inverter interfaced distributed generators. Such an approach is essential for the proper selection and coordination of protective devices for a feeder with distributed generators. Finally, the dissertation illustrates the extent of deterioration a DG can cause on the overcurrent protective relay performance. An approach to solve this problem and restore the overcurrent relay performance is presented.
Design and Characterization of Various Circuit Topologies for Battery/Ultracapacitor Hybrid Energy Storage Systems.
(2010-07-12) Govindaraj, Arvind; Srdjan Lukic, Committee Chair; Subhashish Bhattacharya, Committee Member; Mo-Yuen Chow, Committee Member
Design and Control of a Cascaded H-Bridge Converter based Solid State Transformer (SST).
(2010-08-06) Zhao, Tiefu; Alex Huang, Committee Chair; Mo-Yuen Chow, Committee Member; Mesut Baran, Committee Member; Subhashish Bhattacharya, Committee Member; Fen Wu, Committee Member
Design, Analysis and Realtime Realization of Artificial Neural Network for Control and Classification
(2006-05-11) Dong, Puxuan; Rhett Davis, Committee Member; Wesley Snyder, Committee Member; Mohan Putcha, Committee Member; Griff L. Bilbro, Committee Chair; Mo-Yuen Chow, Committee Member
Artificial neural networks (ANNs) are parallel architectures for processing information even though they are usually realized on general-purpose digital computers. This research has been focused on the design, analysis and real-time realization of artificial neural networks using programmable analog hardware for control and classification. We have investigated field programmable analog arrays (FPAAs) for realizing artificial neural networks (ANN). Our research results and products include a general theoretical limit on the number of neurons required by an ANN to classify a given number of data points, a design methodology for the efficient use of specific FPAA resources in ANN applications, several multi-chip FPAA implementations of ANNs for classification experiments, several single-chip FPAA implementations of analog PID controllers for an unmanned ground vehicle (UGV), experimental evaluation of FPAA PID controllers with a conventional digital PID controller on a UGV, and finally a single-chip FPAA implementation of a (non-linear) ANN controller for comparison with the previous FPAA PID controller on a UGV. 2 These results are collected as four papers formatted for publication and comprising chapters 3, 4, 5, and 6 of this thesis. The first paper develops our general bound for neural network complexity. The second presents a systematic approach based on the upper bound theory for implementing and simplifying neural network structures in FPAA technology. In the third paper, a FPAA based PID controller was designed and characterized in a path-tracking UGV; some of the results from this report are used as a baseline in the fourth paper. In the fourth paper, a FPAA based ANN controller is designed to control a path-tracking UGV and is investigated analytically and with simulation before its performance was experimentally compared to the previously designed FPAA PID controller regarding speed, stability and robustness. In conclusion, this dissertation focuses on the design, analysis and real-time realization of artificial neural networks. The proposed upper bound for neural network complexity provides guidelines for reducing hardware requirements and applies to any layered ANN approach to classification. It is complemented by the neural network structure simplification method which exploits specific features available in the FPAA technology which we used in our experiments and which we believe possess great potential for future real-time control and classification applications.
Flow Aggregation based Lagrangian Relaxation with Applications to Capacity Planning of IP Networks with Multiple Classes of Service
(2004-01-22) Wu, Kehang; Arne A Vouk, Committee Member; Mladen A. Vouk, Committee Member; Mo-Yuen Chow, Committee Member; Douglas S. Reeves, Committee Chair
Nonlinear multicommodity flow problem with integer constraints is an important subject with a wide variety of application contexts. Given the inherent difficulty of solving nonlinear integer programming problems and the lack of efficient systematic approaches, research has primarily been focused on heuristics, which may be inadequate in many cases. In this dissertation, we present a novel Lagrangian Relaxation based approach to attack the nonlinear multicommodity flow problem with integer constraints. Unlike other methods based on the relaxation of integer constraints or nonlinear constraints, our approach relaxes the flow aggregation equation, or the relationship between individual flows and the variable representing the total amount of traffic in each link. The relaxation of the flow aggregation equation makes the resulting Lagrangian dual problem separable to simpler subproblems, which will not be possible if the problem is relaxed otherwise. The subgradient method is used to find the optimal Lagrangian multipliers. The aforementioned method is subsequently applied to three closely related capacity planning problems to verify its effectiveness. We address the problems of link dimensioning and routing optimization for IP networks supporting DiffServ Expedited Forwarding (EF) and best effort (BE) traffic classes. We first study capacity planning in the context of legacy IP networks, where only non-bifurcated routing is allowed for traffic with the same source and destination. In the second problem, we assume the use of Multiprotocal Label Switching (MPLS), and therefore allow bifurcated routing. The third problem deals with the design of survivable MPLS networks, where spare capacity is required. We investigated experimentally the solution quality and running time of this approach. The results from our experiments indicate that our method produces solutions that are within a few percent of the optimal solution, while the running time remains reasonable on practical-sized networks. This represents the first work for capacity planning of multi-class IP networks with non-linear performance constraints and discrete link capacity constraints. We also expect the solution method to be a promising approach for other non-linear multicommodity flow problems with integer constraints.
Flux and Torque Estimation in Direct Torque Controlled (DTC)Induction Motor Drive.
(2010-11-03) Ballal, Siddharth; Srdjan Lukic, Committee Chair; Subhashish Bhattacharya, Committee Member; Mo-Yuen Chow, Committee Member
Global Optimization Methods for Adaptive IIR Filters
(2008-07-20) Ocloo, Senanu Kofi; William Edmonson, Committee Chair; Mo-Yuen Chow, Committee Member; Ethelbert Chukwu, Committee Member; Winser Alexander, Committee Member
Adaptive filtering systems mimic the ability of biological systems to change their internal configuration so as to better survive in their environment. This ability is critical because adaptive filters operate in noisy, time-varying environments. At design time, although performance objectives are well-defined, there is limited a priori information about the characteristics of the input signals. As a result, systems capable of meeting performance specifications while operating under such conditions need to be able to make on-the-fly changes to their structure so as to constantly improve performance. Over the last couple of decades, their efficacy and robustness have been demonstrated in numerous applications and today, they are used in a wide variety of applications ranging from radar, sonar and active noise control to channel equalization, adaptive antenna systems and hearing aids. Adaptive IIR filters provide significant advantages over equivalent adaptive FIR filters implementations. First, they more accurately model physical plants that have pole-zero structures. Secondly, they are typically capable of meeting performance specifications using fewer filter parameters. This savings in parameters, which can be as much as 5 to 10 times, leads to the use of fewer multiplier blocks and therefore, lower power consumption. Despite these advantages, adaptive IIR filters have not found widespread use because the associated Mean Squared Error (MSE) cost function is multimodal and therefore, significantly difficult to minimize. Additionally, the filter can become unstable during adaptation. These two properties pose several problems for adaptive algorithms, causing them to be sensitive to initial conditions, produce biased solutions, unstable filter configurations or converge to local minima. These problems prevent the widespread use of adaptive IIR filters in practice and if such filter structures are to become more practical, new, innovative solutions are required. This dissertation proposes a new algorithm for minimizing the MSE cost function of adaptive IIR filters, aimed at addressing some of the aforementioned issues. We adopt the approach of using a Branch-and-Bound algorithm because it is guaranteed to locate global minima. Furthermore, we employ interval arithmetic for all computations. Its use allows for all numerical errors that accrue during computations to be accounted for. Simulation results show that the resulting algorithm is a viable one, and when compared to a number of existing, state-of-the-art algorithms, outperforms them in a number of categories.
Power System Dynamic Voltage Management with Advanced STATCOM and Energy Storage System
(2007-12-19) Han, Chong; Subhashish Bhattacharya, Committee Member; Mo-Yuen Chow, Committee Member; Mesut E. Baran, Committee Member; Tomislav Vukina, Committee Member; Alex Q. Huang, Committee Chair
STATCOM (static synchronous compensator) and ESS (energy storage system), as a shunt-link flexible AC transmission system (FACTS) controller, has shown extensive feasibility in terms of cost-effectiveness in a wide range of problem-solving abilities from transmission to distribution levels. Recently, advances in power electronics technologies, such as the emerging kilohertz high power semiconductor switches, cascaded multilevel converter (CMC) topology comprising modular H-bridge voltage source converter (VSC), and digital control technology, have the potential to achieve the modularity and scalability design, lower the overall cost, and improve the reliability and functionality of power electronics-based controllers, hence, resulting in increasing applications of STATCOM⁄ESS. However, how to control a CMC-based STATCOM⁄ESS to realize excellent performance, high reliability and low cost poses the challenges to researchers. On the other side, renewable large wind farms, as a fast growing power generation method, undergoes its inherent power quality and stability issues. How to develop the effective and accurate model of a wind power system and how to control STATCOM for wind power support are not in a classical textbook and posing challenges. Meanwhile, as an industrial customer of utilities, electrical arc furnace (EAF) is the major flicker source that degrades the grid power quality. How to economically and efficiently mitigate EAF flicker is a tough issue bothering utility professionals. As a promising solution, a high bandwidth STATCOM⁄ESS can be used. This dissertation is dedicated to a comprehensive study of CMC-based STATCOM and its ultracapacitor energy storage system (UESS), and its applications in power system dynamic voltage management, specifically, wind farm voltage fluctuations suppressions and EAF flicker mitigation. The goal of this dissertation is to achieve high-performance, reliable, flexible, cost-effective controllers of the CMC-based STATCOM⁄UESS for the specific challenging utility applications. Major contributions proposed in this dissertation include: 1) STATCOM model analysis and compensator design guideline; 2) STATCOM AC-side energization and de-energization; 3) STATCOM per-phase DC voltage balancing; 4) wind farm voltage fluctuation suppression; 5) EAF flicker mitigation; 6) modeling, integration and control of UESS.