System level power integrity transient analysis using a physics-based approach

With decreasing supply voltage level and massive demanding current on system chipset, power integrity design becomes more and more critical for system stability. The ultimate goal of well-designed power delivery network (PDN) is to deliver desired voltage level from the source to destination, in other words, to minimize voltage noise delivered to digital devices. The thesis is composed of three parts. The first part focuses on-die level power models including simplified chip power model (CPM) for system level analysis and the worst scenario current profile. The second part of this work introduces the physics-based equivalent circuit model to simplify the passive PDN model to RLC circuit netlist, to be compatible with any spice simulators and tremendously boost simulation speed. Then a novel system/chip level end-to-end transient model is proposed, including the die model and passive PDN model discussed in previous two chapters as well as a SIMPLIS based small signal VRM model. In the last part of the thesis, how to model voltage regulator module (VRM) is explicitly discussed. Different linear approximated VRM modeling approaches have been compared with the SIMPLIS small signal VRM model in both frequency domain and time domain. The comparison provides PI engineers a guideline to choose specific VRM model under specific circumstances. Finally yet importantly, a PDN optimization example was given. Other than previous PDN optimization approaches, a novel hybrid target impedance concept was proposed in this thesis, in order to improve system level PDN optimization process --Abstract, page iv

Circuit design and analysis for on-FPGA communication systems

On-chip communication system has emerged as a prominently important subject in Very-Large- Scale-Integration (VLSI) design, as the trend of technology scaling favours logics more than interconnects. Interconnects often dictates the system performance, and, therefore, research for new methodologies and system architectures that deliver high-performance communication services across the chip is mandatory. The interconnect challenge is exacerbated in Field-Programmable Gate Array (FPGA), as a type of ASIC where the hardware can be programmed post-fabrication. Communication across an FPGA will be deteriorating as a result of interconnect scaling. The programmable fabrics, switches and the specific routing architecture also introduce additional latency and bandwidth degradation further hindering intra-chip communication performance. Past research efforts mainly focused on optimizing logic elements and functional units in FPGAs. Communication with programmable interconnect received little attention and is inadequately understood. This thesis is among the first to research on-chip communication systems that are built on top of programmable fabrics and proposes methodologies to maximize the interconnect throughput performance. There are three major contributions in this thesis: (i) an analysis of on-chip interconnect fringing, which degrades the bandwidth of communication channels due to routing congestions in reconfigurable architectures; (ii) a new analogue wave signalling scheme that significantly improves the interconnect throughput by exploiting the fundamental electrical characteristics of the reconfigurable interconnect structures. This new scheme can potentially mitigate the interconnect scaling challenges. (iii) a novel Dynamic Programming (DP)-network to provide adaptive routing in network-on-chip (NoC) systems. The DP-network architecture performs runtime optimization for route planning and dynamic routing which, effectively utilizes the in-silicon bandwidth. This thesis explores a new horizon in reconfigurable system design, in which new methodologies and concepts are proposed to enhance the on-FPGA communication throughput performance that is of vital importance in new technology processes

A Data Analytics Framework for Smart Grids: Spatio-temporal Wind Power Analysis and Synchrophasor Data Mining

abstract: Under the framework of intelligent management of power grids by leveraging advanced information, communication and control technologies, a primary objective of this study is to develop novel data mining and data processing schemes for several critical applications that can enhance the reliability of power systems. Specifically, this study is broadly organized into the following two parts: I) spatio-temporal wind power analysis for wind generation forecast and integration, and II) data mining and information fusion of synchrophasor measurements toward secure power grids. Part I is centered around wind power generation forecast and integration. First, a spatio-temporal analysis approach for short-term wind farm generation forecasting is proposed. Specifically, using extensive measurement data from an actual wind farm, the probability distribution and the level crossing rate of wind farm generation are characterized using tools from graphical learning and time-series analysis. Built on these spatial and temporal characterizations, finite state Markov chain models are developed, and a point forecast of wind farm generation is derived using the Markov chains. Then, multi-timescale scheduling and dispatch with stochastic wind generation and opportunistic demand response is investigated. Part II focuses on incorporating the emerging synchrophasor technology into the security assessment and the post-disturbance fault diagnosis of power systems. First, a data-mining framework is developed for on-line dynamic security assessment by using adaptive ensemble decision tree learning of real-time synchrophasor measurements. Under this framework, novel on-line dynamic security assessment schemes are devised, aiming to handle various factors (including variations of operating conditions, forced system topology change, and loss of critical synchrophasor measurements) that can have significant impact on the performance of conventional data-mining based on-line DSA schemes. Then, in the context of post-disturbance analysis, fault detection and localization of line outage is investigated using a dependency graph approach. It is shown that a dependency graph for voltage phase angles can be built according to the interconnection structure of power system, and line outage events can be detected and localized through networked data fusion of the synchrophasor measurements collected from multiple locations of power grids. Along a more practical avenue, a decentralized networked data fusion scheme is proposed for efficient fault detection and localization.Dissertation/ThesisPh.D. Electrical Engineering 201

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavy-duty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling approaches for the FCS, battery, ultracapacitor, and further relevant subsystems are briefly described. Afterward, common methodologies for EMS are structured, presenting a new taxonomy for dynamic optimization-based EMS from a control engineering perspective. Finally, the findings lead to a guideline toward holistic EMS, encouraging the co-optimization of system design, and EMS development for FCHEVs. For the EMS, we propose a layered model predictive control (MPC) approach, which takes velocity planning, the mitigation of degradation effects, and the auxiliaries into account simultaneously

SIZING OPTIMIZATION OF STANDALONE PHOTOVOLTAIC SYSTEM FOR RESIDENTIAL LIGHTING

This study presents a sizing methodology to optimize photovoltaic array and battery storage in a standalone photovoltaic (PV) system with lighting load. Sizing using the deterministic method was carried out for initial design. Stochastic method by mean of Loss of Power Supply Probability (LPSP) calculation was developed using daily weather data input. The weather data consists of ambient temperature, relative humidity and solar radiation. An estimation method of hourly solar radiation data is also developed in this study to complete any missing solar radiation data in the dataset. Then, complete time-series dataset can be used for LPSP calculation and system simulation. Size optimization was carried out using a graphical method. System size was optimized based on desired system performance (indicated by LPSP value) and minimum cost that consist of Capital Cost and Life-cycle cost. In addition, Excess energy value is used as over-design indicator. A comparison of sizing optimization methods was carried out, and it was found that the design space approach giving flexibility in the selection of PV panel and battery capacity compared to other methods. The sizing result of this method is a configuration of 500Wp PV array and 400Ah battery. Economic analyses, including cost of the energy and payback period for selected configuration are studied. A simulation model was developed using TRNSYS 16 to test the sizing results. Experimental data was used to validate the simulation results. Simulation validation shows that result accuracy has improved. The results indicated that Root Mean Square Error (RMSE) between simulated and measured battery voltage has decreased by 50% compared to previous work. The validated simulation model then was used to test and analyse the selected PV system configuration