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- Genre: Academic theses
Description
Voltage Source Converters (VSCs) have been widely used in grid-connected applications with Distributed Energy Resource (DER) and Electric Vehicle (EV) applications. Replacement of traditional thyristors with Silicon/Silicon-Carbide based active switches provides full control capability to the converters and allows bidirectional power flow between the source and active loads. In this study, advanced control strategies for DER inverters and EV traction inverters will be explored.Chapter 1 gives a brief introduction to State-of-the-Art of VSC control strategies and summarizes the existing challenges in different applications. Chapter 2 presents multiple advanced control strategies of grid-connected DER inverters. Various grid support functions have been implemented in simulations and hardware experiments under both normal and abnormal operating conditions. Chapter 3 proposes an automated design and optimization process of a robust H-infinity controller to address the stability issue of grid-connected inverters caused by grid impedance variation. The principle of the controller synthesis is to select appropriate weighting functions to shape the systems closed-loop transfer function and to achieve robust stability and robust performance. An optimal controller will be selected by using a 2-Dimensional Pareto Front. Chapter 4 proposes a high-performance 4-layer communication architecture to facilitate the control of a large distribution network with high Photovoltaic (PV) penetration. Multiple strategies have been implemented to address the challenges
of coordination between communication and system control and between different communication protocols, which leads to a boost in the communication efficiency and makes the architecture highly scalable, adaptive, and robust. Chapter 5 presents the control strategies of a traditional Modular Multilevel Converter (MMC) and a novel Modular Isolated Multilevel Converter (MIMC) in grid-connected and variable speed drive applications. The proposed MIMC is able to achieve great size reduction for the submodule capacitors since the fundamental and double-line frequency voltage ripple has been cancelled. Chapter 6 shows a detailed hardware and controller design for a 48 V Belt-driven Starter Generator (BSG) inverter using automotive gate driver ICs and microcontroller. The inverter prototype has reached a power density of 333
W/inch3, up to 200 A phase current and 600 Hz output frequency.
ContributorsSi, Yunpeng (Author) / Lei, Qin (Thesis advisor) / Ayyanar, Raja (Committee member) / Vittal, Vijay (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2022
Description
Nowadays, wireless communications and networks have been widely used in our daily lives. One of the most important topics related to networking research is using optimization tools to improve the utilization of network resources. In this dissertation, we concentrate on optimization for resource-constrained wireless networks, and study two fundamental resource-allocation problems: 1) distributed routing optimization and 2) anypath routing optimization. The study on the distributed routing optimization problem is composed of two main thrusts, targeted at understanding distributed routing and resource optimization for multihop wireless networks. The first thrust is dedicated to understanding the impact of full-duplex transmission on wireless network resource optimization. We propose two provably good distributed algorithms to optimize the resources in a full-duplex wireless network. We prove their optimality and also provide network status analysis using dual space information. The second thrust is dedicated to understanding the influence of network entity load constraints on network resource allocation and routing computation. We propose a provably good distributed algorithm to allocate wireless resources. In addition, we propose a new subgradient optimization framework, which can provide findgrained convergence, optimality, and dual space information at each iteration. This framework can provide a useful theoretical foundation for many networking optimization problems. The study on the anypath routing optimization problem is composed of two main thrusts. The first thrust is dedicated to understanding the computational complexity of multi-constrained anypath routing and designing approximate solutions. We prove that this problem is NP-hard when the number of constraints is larger than one. We present two polynomial time K-approximation algorithms. One is a centralized algorithm while the other one is a distributed algorithm. For the second thrust, we study directional anypath routing and present a cross-layer design of MAC and routing. For the MAC layer, we present a directional anycast MAC. For the routing layer, we propose two polynomial time routing algorithms to compute directional anypaths based on two antenna models, and prove their ptimality based on the packet delivery ratio metric.
ContributorsFang, Xi (Author) / Xue, Guoliang (Thesis advisor) / Yau, Sik-Sang (Committee member) / Ye, Jieping (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the proliferation of mobile computing and Internet-of-Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions of Bytes of data at the network edge. Driving by this trend, there is an urgent need to push the artificial intelligence (AI) frontiers to the network edge to unleash the potential of the edge big data fully. This dissertation aims to comprehensively study collaborative learning and optimization algorithms to build a foundation of edge intelligence. Under this common theme, this dissertation is broadly organized into three parts. The first part of this study focuses on model learning with limited data and limited computing capability at the network edge. A global model initialization is first obtained by running federated learning (FL) across many edge devices, based on which a semi-supervised algorithm is devised for an edge device to carry out quick adaptation, aiming to address the insufficiency of labeled data and to learn a personalized model efficiently. In the second part of this study, collaborative learning between the edge and the cloud is studied to achieve real-time edge intelligence. More specifically, a distributionally robust optimization (DRO) approach is proposed to enable the synergy between local data processing and cloud knowledge transfer. Two attractive uncertainty models are investigated corresponding to the cloud knowledge transfer: the distribution uncertainty set based on the cloud data distribution and the prior distribution of the edge model conditioned on the cloud model. Collaborative learning algorithms are developed along this line. The final part focuses on developing an offline model-based safe Inverse Reinforcement Learning (IRL) algorithm for connected Autonomous Vehicles (AVs). A reward penalty is introduced to penalize unsafe states, and a risk-measure-based approach is proposed to mitigate the model uncertainty introduced by offline training. The experimental results demonstrate the improvement of the proposed algorithm over the existing baselines in terms of cumulative rewards.
ContributorsZhang, Zhaofeng (Author) / Zhang, Junshan (Thesis advisor) / Zhang, Yanchao (Thesis advisor) / Dasarathy, Gautam (Committee member) / Fan, Deliang (Committee member) / Arizona State University (Publisher)
Created2023