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Title: Economic and Computational Methods for the Control of Uncertain Systems
Creator: Zhang, Jin
Date: 2019
Description: The Economic Linear Optimal Control (ELOC) can improve the effective use of economic and dynamic information throughout the traditional...
Show moreThe Economic Linear Optimal Control (ELOC) can improve the effective use of economic and dynamic information throughout the traditional optimization and control hierarchy. This dissertation investigates the computational procedures used to obtain a global solution to the ELOC problem. The proposed method employs the Generalized Benders Decomposition (GBD) algorithm. Compared to the previous branch and bound approach, the application of GBD to the ELOC problem will greatly improve computational performance. A technological benefit of decomposing the problem into steady-state and dynamic parts is the ability to utilize nonlinear steady-state models, since the relaxed master problem is free of SDP type constraints and can be solved using any global nonlinear programming algorithm.In order to address the issue of model/plant mismatch, the dissertation will also investigate how to handle box-type uncertainties in ELOC. We consider two methods, a robust formulation for when the uncertainty is completely unknown and a Linear Parameter Varying formulation for when uncertainty can be measured in real time. In both cases, the infinite number of conditions that need to be satisfied are reduced to a finite set of constraints. The resulting problem formulations have a similar structure to the ELOC and can be solved globally by employing the generalized Benders decomposition.Despite a high-quality control law, the ultimate performance of closed-loop systems will be dictated by the quality and limitation of hardware element. Thus, hardware selection is also investigated in the dissertation. The cost-optimal hardware selection problem has been shown to be of the Mixed Integer Convex Programming (MICP) class. While such a formulation provides a route to global optimality, use of the branch and bound search procedure has limited application to fairly small systems. In this dissertation, we illustrate that a simple reformulation of the MICP and subsequent application of the GBD algorithm will result in massive reductions in computational effort.Finally, the problems of value-optimal sensor network design (SND) for steady-state and closed-loop systems are investigated. The value-optimal SND problem has been shown to be of the nonconvex mixed integer programming class. In the dissertation, it is demonstrated after transforming into an equivalent reformation, the application of GBD algorithm will significantly reduce the computational effort.
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Title: Numerical and Experimental Investigation to Improve Radio Frequency Performance of Photonic Band Gap Accelerating Structure
Creator: Zhou, Ning
Date: 2019
Description: In this thesis, the design and experimental work of a Photonic Band Gap (PBG) accelerator cavity with star-shape array is presented. Photonic...
Show moreIn this thesis, the design and experimental work of a Photonic Band Gap (PBG) accelerator cavity with star-shape array is presented. Photonic band gap structures (metallic and/ or dielectric) have been proposed for accelerator applications. These structures act like filters, allowing electromagnetic waves propagating at some frequencies to be transmitted through the lattice, while rejecting the RF fields in some (unwanted) frequency range. Additionally PBG structures are used to support selective field patterns (modes) in a resonator or waveguide by a defect region within the lattice; while damping unwanted higher- or lower-order modes without impacting the supported mode. The unwanted modes affect beam propagation or even distort the beam. Thus, suppression of unwanted modes is important. In this thesis work, a star shape structure is obtained from removing elements in a PBG structure with triangular lattice and employed for integration with a metallic cavity resonator for accelerator applications. Impedance matching is accomplished by adjustment of positions of some elements in the array. The design was fabricated and measured to have an input return loss of over 30 dB at the targeted frequency of 11.4GHz. The measured results are in an excellent agreement with the computer simulation.
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Title: Scalable Non-Intrusive Load Monitoring
Creator: Zhuang, Mengmeng
Date: 2019
Description: Load Monitoring (LM) is a fundamental step to implement sustainable energy conservation. LM includes Intrusive LM (ILM) and Non-Intrusive LM ...
Show moreLoad Monitoring (LM) is a fundamental step to implement sustainable energy conservation. LM includes Intrusive LM (ILM) and Non-Intrusive LM (NILM). Real time feedback and informed advice to customers obtained from refined energy consumption can greatly improve energy efficiency towards sustainable energy conservation. Compared with intrusive approaches, non-intrusive approaches enjoy low cost, easy installation, and promising scalable commercialization potentials via elaborated data obtained from NILM. However, large-scale NILM deployments are facing challenges mainly including theoretical research and innovative applications. For theoretical research, there is still no generalized model to distinguish multiple-mode appliances, similar, or unknown appliances, and there is still no universal performance metrics to evaluate various NILM algorithms, especially for some unsupervised algorithms. For innovative applications, cost and user engagement are the two most important factors to limit the scalability of NILM. Scalable NILM refers to load disaggregation model that can be generalized and that has various application scenarios in a large-scale deployment. With the main objective of achieving scalable NILM, we focus on a semi-supervised generalized load disaggregation model and innovative applications including Proactive Demand Response (PDR) and energy information recommendation for enabling action towards sustainable energy conservation. Furthermore, in order to achieve sustainable energy conservation, we develop scalable NILM system and propose a user-centered comprehensive application platform Energy (ABC)2 to seek solutions from technology aspect and user engagement. On one hand, we propose an innovative virtual closed loop control concept model with human behaviors as virtual feedback controller and apply Deep Reinforcement Learning (DRL) approaches into DR decision management and personalized energy aware recommendation towards sustainable energy conservation. On the other hand, we develop and implement NILM deployment in China and propose an innovative idea on user engagement and data sharing solution business model, namely Energy Data Sharing Platform (EDSP), and design a scheme to strengthen the scalability of NILM towards a sustainable energy future.
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Title: Value of DER to distribution networks
Creator: Nasiri, Hiva
Date: 2019
Description: Distributed energy resources (DERs) provide various values to electric power systems. One of the challenges DER introduce is to determine what...
Show moreDistributed energy resources (DERs) provide various values to electric power systems. One of the challenges DER introduce is to determine what is the value of DER contributions to various sectors in distributed power systems. Analyzing such challenges require understanding the inherent variability of DERs and the uncertainty in hourly load. The value of DER is also a function of network conditions and its adaptability for responding to DERs and loading variations.The uncertain conditions in the planning and the operation of electric power systems make the DER valuation a complicated task which requires sophisticated analytical methods. DER valuation will be a critical issue for the power market operation as we try to determine the most economical and reliable generation portfolio to maximize social welfare. In the DER valuation, there is a stage in which the DER candidates are not located or sized properly, and the utility will provide an approximated solution in order to value DERs. In this thesis, DER valuation has been addressed initially using a stochastic analysis in order to determine a range of values. Once the range is determined, we calculate the economic viability of the values within the range. If DER can reduce the upgrade cost for delivering the load in a distribution system, then the proposed size and the location of DER will have viability. In addition, with the proposed method, a utility can decide on the cost and the methodology of mitigating contingencies by DERs and allocate that cost to the amount of DER for valuation. This is considered as the second phase of this valuation method.As the final phase of this thesis, different types of financial contracts are considered as part of valuation which can introduce additional merits to the utility operation. Recently, the concept of real options has been considered in electric power system projects. The real option analyses (ROA) concept has proven to be viable because of the variability in DERs and changes in real-time load as discussed in this dissertation. The DER valuation is highly case sensitive, and many special factors may have an effect on its value. In this thesis, the factors with the highest impact have been considered, however, there could be additional factors with lower impacts which usually depend on the utility planning and operation. The essence of such factors is further discussed as part of the future work are briefly explained at the end of the thesis.
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Title: NON-INTRUSIVE LOAD MONITORING IN RESIDENTIAL BUILDING
Creator: Lu, Mengqi
Date: 2019
Description: Non-Intrusive Load Monitoring (NILM) is an important application to monitor household appliance activities and provide related information to...
Show moreNon-Intrusive Load Monitoring (NILM) is an important application to monitor household appliance activities and provide related information to house owner or/and utility company via a single sensor installed at the electrical entry of the house. With this information, utilities can do many tasks such as energy conservation, planning generation more wisely, and demand response (DR) study. For house owners, they can understand their bill more clearly and make monthly budget plan. For researchers, NILM system is a good way to do the energy management in buildings and help to provide power information for smart homes design. Thus, an increasing number of new algorithms have been developed in recent years. In these algorithms, researchers either use existing public datasets or collect their own data which causes such problems as insufficiency of electrical parameters, missing of ground-truth data, absence of many appliances, and lack of appliance information. To solve these problems, this dissertation presents a model-based platform for NILM system development, namely Functional Intrusive Load Monitor (FILM). By using this platform, the state transitions and activities of all the involved appliances can be preset by researchers, and multiple electrical parameters such as harmonics and power factor can be monitored or calculated. This platform will help researchers save the time of collecting experimental data, utilize precise control of individual appliance activities, and develop load signatures of devices. Moreover, event detection, as an important part of event-based NILM methods, has a direct impact on the accuracy of the ultimate load disaggregation results in the entire NILM framework. This dissertation also presents a hybrid event detection approach for relatively complex household load datasets that include appliances with long transients, high fluctuations, and/or near-simultaneous actions. The structure, steps, and working principle of this approach are described in detail. The proposed approach does not require additional information about household appliances, nor does it require any training sets.Case studies on different datasets are conducted to evaluate the performance of the proposed approach in comparison with several existing approaches including log likelihood ratio detector with maxima (LLD-Max) approach, active window-based (AWB) approach, and generalized likelihood ratio (GLR) approach. Results show that the proposed approach works well in detecting events in complex household load datasets and performs better than the existing approaches.
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Title: Towards the Robust Situation Awareness in Distribution Management System
Creator: Yao, Yiyun
Date: 2019
Description: In distribution systems, intermittent distributed energy resources (DERs) and vol-atile loads will result in a wide variation of system...
Show moreIn distribution systems, intermittent distributed energy resources (DERs) and vol-atile loads will result in a wide variation of system operating conditions. This motivates the establishment of modern distribution management system (DMS) for real-time net-work monitoring, resource optimization, and demand management. Three subproblems are mainly discussed when establishing the robust situation awareness in DMS. A measurement placement problem is proposed to decide the optimal locations and types of measurements to be placed in the distribution systems that minimize the worst-case estimation errors for DSSE over different system operating conditions. Four indices of the estimation error covariance matrix are chosen as the criteria of accuracy. The proposed measurement placement problem is formulated as a mixed-integer sem-idefinite programming (MISDP) problem. To avoid the combinatorial complexity, a con-vex relaxation, followed by a local optimization method, is employed to solve the MISDP problem. The proposed problem and the effectiveness of the proposed solution method are numerically demonstrated on the 33-bus distribution system.Distribution system state estimation (DSSE) is one of the vital components in the next-generation distribution management system (DMS), which allows the operators to monitor the entire system’s operating conditions. Due to the lack of real-time measurements, DSSE has to process measurements whose quality varies significantly across different sources, which causes convergence issue to the Gauss-Newton solver. In this chapter, a semidefinite programming (SDP) framework is developed to reformulate the DSSE problem into a rank- constrained SDP problem. One challenge of this technique is the nonconvex rank-one constraint, which is generally relaxed. However, the relaxed SDP-DSSE problem cannot guarantee a rank-one solution and hence lose optimality. Therefore, we propose two solution approaches, namely the rank reduction approach and the convex iteration approach, to obtain rank-one solutions for the SDP-DSSE problem. The proposed model and the effectiveness of the proposed solution approaches are numerically demonstrated on the IEEE 13-, 34-bus, and 123-bus distribution systems.A SE algorithm based on random measurements selection, which is inspired by the concept of moving target defense (MTD), is developed to prevent and mitigate stealthy cyber-attacks. With the proposed SE, a library of selected measurements scenarios is first generated offline given the available measurements and network topology. During online operation, multiple weighted least square (WLS) based SEs are processed in parallel with randomly picked scenarios from the library. The final solution is selected based on the largest normalized residuals with regard to individual scenarios. The effectiveness of the proposed SE is examined by attack-defense experiments on IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems.
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Title: MICROGRID COMMUNICATION: HARDWARE AND SOFTWARE TECHNOLOGIES
Creator: GONG, WENLONG
Date: 2019
Description: The Keating Nanogrid at Illinois Institute of Technology (IIT) was designed to be an islandable ac/dc hybrid nanogrid. The on-site rooftop...
Show moreThe Keating Nanogrid at Illinois Institute of Technology (IIT) was designed to be an islandable ac/dc hybrid nanogrid. The on-site rooftop solar and battery system are supporting the interconnected dc and ac subsystems. The nanogrid system at the ac bus is eventually interconnected with the IIT Microgrid. The battery storage system at Keating Nanogrid was designed to support its critical loads for about 8 hours daily. A battery management system (BMS) was employed so that it can monitor and report storage system status to the Keating Nanogrid controller for optimal decision making.The dc load including 94 fixtures controllable LED lighting system was designed to replace the original 189 fixtures ac florescent lighting system. The LEDs’ dc-dc driver was designed and built to enable the dc input provided by the rooftop solar photovoltaic system. The dc system control and communication module was designed and built to make the LED lights controllable individually by the Keating Nanogrid controller or sensor network.To enhance safety at night, 4 islandable LED streetlights were deployed on the east side of the Keating Nanogrid where grid connection was not available for lighting. The east-side streetlight is self-sustained with its own wind turbine, solar panel and battery. The real-time monitoring system was designed and built for the streetlights.The Keating Nanogrid was designed for multiple purposes including the monitoring and control of all elements via pertinent communication pathways. It exchanges the real-time information with the IIT Microgrid and together they make optimal operation decisions to enhance efficiency and reliability of the entire IIT Microgrid system.
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Title: LOW DIMENSIONAL SIGNAL SETS FOR RADAR APPLICATIONS
Creator: Alphonse Joseph Rajkumar, Sebastian Anand
Date: 2018
Description: In this dissertation we present a view in which the radar signals as the elements of a high dimensional signal set. The dimension is equal to...
Show moreIn this dissertation we present a view in which the radar signals as the elements of a high dimensional signal set. The dimension is equal to the number of discrete samples (M) of the signal. Because the radar signals should satisfy certain conditions for good performance, most lie in much smaller subsets or subspaces. By developing appropriate lower dimensional signal spaces that approximate these areas where the radar signals live, we can realize potential advantage because of the greater parametric simplicity. In this dissertation we apply this low dimensional signal concept in radar signal processing. In particular we focus on radar signal design and radar signal estimation. Signal design comes under radar measures and signal estimation comes under radar countermeasures.In signal design problem one searches for the signal element that has smaller sidelobes and also satisfies certain constraints such as bandwidth occupancy, AC mainlobe width, etc. The sidelobe levels are quantified by Peak Sidelobe Ratio (PSLR) and Integrated Sidelobe Ratio (ISLR). We use linear combination of these two metrics as the cost function to determine the quality of the designed signal. There is a lot of effort in designing parameterized signal sets including our proposed Asymmetric Time Exponentiated Frequency Modulated (ATEFM) signal and Odd Polynomial FrequencySignal (OPFS). Our contribution is to demonstrate that the best signal elements from these low dimensional signal sets (LDSS) mostly outperform the best signal elements that are randomly chosen from the radar signal subset with dimensionality M. Since searching the best signal element from the LDSS requires less computational resources it is prudent to search for the best signal elements from the low dimensional signal sets.In signal estimation problem we try to estimate the signal transmitted by a noncooperating radar which is intercepted by multiple passive sensors. The intercepted signals often have low SNR and there could be only few intercepted signals available for signal estimation. Predominantly used method for estimating the radar signals is Principal Component Analysis (PCA). When the SNR is low (< 0 dB) we need large number of intercepted signals to get an accurate estimates from PCA method. Our contribution is to demonstrate that by limiting the search for the best signal estimate within the low dimensional signal sets one can get more accurate estimates of the unknown transmitted signal at low SNRs with smaller number of sensors compared to PCA.
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Title: Distributed Resource Management for Wireless Networks Over Unlicensed Spectrum
Creator: Han, Mengqi
Date: 2020
Description: In the past decades, a variety of wireless networks have been deployed, e.g., long term evolution (LTE) cellular networks, wireless local...
Show moreIn the past decades, a variety of wireless networks have been deployed, e.g., long term evolution (LTE) cellular networks, wireless local network networks (WLANs), cloud radio access network (C-RANs), wireless metropolitan area networks (WMANs), wireless body area networks (WBANs) and etc.To meet the exponential growth of traffic demands and improve the network throughput, different enhancement in the MAC protocols have been proposed for the emerging networks. For example, U-LTE (Unlicensed LTE) is proposed for LTE users to aggregate the spacious unlicensed spectrum with the licensed spectrum to boost the network throughput. Meanwhile, Wi-Fi users are allowed to opportunistically bond available channels for high data rate transmissions to improve the spectrum efficiency and network throughput. But the performance of the emerging networks with the new techniques has not been well investigated. Thus, in this thesis, we comprehensively investigate the network performance in different network scenarios. In each scenario, we first develop mathematical models to identify the performance bottlenecks in the existing MAC protocols. We then propose an algorithm to intelligently tune the protocol parameters to maximize network performance. Finally, the proposed algorithm is compared with some existing algorithms. Specifically, in the first scenario, we evaluate the coexistence performance between the Wi-Fi users with channel bonding capability and the legacy users without channel bonding capability. Specifically, the channel bonding probability and the channel access delay of wireless users are first analyzed, considering the contentions among legacy and multi-channel users in the same channel and across multiple channels. Based on the analysis, the network capacity, i.e., the maximum number of traffic flows that can be supported with the bounded delay performance in a multi-channel Wi-Fi with and without channel bonding, is then derived. Based on the analytical results, we propose a heuristic bonding policythat can provide important guidelines to control the number of flows to satisfy the QoS requirement and achieve the maximum network capacity. In addition, we propose an efficient probabilistic channel aggregation scheme to maximize the network throughput under the quality of service constraints for multi-channel users with channel aggregation capability. A Proximal Policy Optimization (PPO) based approach is further applied to intelligently tune the aggregating probabilities of secondary channels to maximize the network throughput.In the second scenario, we consider that U-LTE users are coexisting with the legacy users without channel bonding capability in the same unlicensed spectrum. The throughput of both Wi-Fi and U-LTE users are both derived when U-LTE users adopting two Load Based Equipment(LBE) random access protocols and Category 4 (Cat 4) algorithm agreed in 3GPP release 13.Based on the analysis, we find that the current protocols of U-LTE users are far from perfect to achieve harmony coexistence. Subject to the system fairness constraint, the aggregate throughput of U-LTE and Wi-Fi networks is maximized based on a semi branch and bound algorithm. To make the complex optimization tractable, reinforcement learning techniques are introduced to intelligently tune the contention window size for both U-LTE and Wi-Fi users. Specifically, a cooperative learning algorithm is developed assuming that the information between different systems is exchangeable. A non-cooperative version is subsequently developed to remove the previous assumption for better practicability. Extensive simulations are conducted to demonstrate the performance of the proposed learning algorithms in contrast to the analytical upper bound under various conditions. It is shown that both proposed learning algorithms can significantly improve the total throughput performance while satisfying the fairness constraints.Finally, by considering the energy constraints, we consider an IoT network where IoT devices use adaptive p-persistent ALOHA for data transmissions. In an IoT network with energy harvesting, an IoT device can contend for channel access only when it is ready, i.e., it has data for transmission and it harvests enough energy for communications. Due to stochastic energy harvesting and random access, the number of ready devices in the network may vary. As such, an analytical framework is first developed using a discrete Markov model to analyze the average number of ready devices. Next, an optimization problem is formulated to maximize the system throughput by adapting the transmission probability p of IoT devices. Given that the wireless environment is unknown at different IoT devices, e.g., the total number of contending devices, data arrival rates of other IoT devices, a multi-agent reinforcement learning algorithm is introduced for each device to autonomously tune the transmission probability in a distributed manner. In addition, game theory is applied to design the reward function to ensure an equilibrium and to closely approach the optimal parameter setting. Numerical results show that the proposed learning algorithm can greatly improve the throughput performance comparing with other algorithms.
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Title: Reconfigurable High-Performance Computation and Communication Platform for Ultrasonic Applications
Creator: Wang, Boyang
Date: 2021
Description: In industrial and medical applications, ultrasonic signals are used in nondestructive testing (NDT), medical imaging, navigation, and...
Show moreIn industrial and medical applications, ultrasonic signals are used in nondestructive testing (NDT), medical imaging, navigation, and communication. This study presents the architecture of high-performance computational systems designed for ultrasonic nondestructive testing, data compression using machine learning, and a multilayer perceptron neural network for ultrasonic flaw detection and grain size characterization. We researched and developed a real-time software-defined ultrasonic communication system for transmitting information through highly reverberant and dispersive solid channels. Orthogonal frequency-division multiplexing is explored to combat the severe multipath effect in the solid channels and achieve an optimal bitrate solution. In this study, a reconfigurable, high-performance, low-cost, and real-time ultrasonic data acquisition and signal processing platform is designed based on an all-programmable system-on-chip (APSoC). We designed the unsupervised learning models using wavelet packet transformation optimized by convolutional autoencoder for massive ultrasonic data compression. The proposed learning models can achieve a compression accuracy of 98% by using only 6% of the original data. For ultrasonic signal analysis in NDT applications, we utilized the multilayer perceptron neural network (MLPNN) to detect flaw echoes masked by strong microstructure scattering noise (i.e., about zero dB SNR or less) with detection accuracy above 99%. It is of high interest to characterize materials using ultrasonic scattering properties for grain size estimation and classification. We successfully designed an MLPNN to classify the grain sizes of materials with an accuracy of 99%. Furthermore, a software-defined ultrasonic communication system based on the APSoC is designed for real-time data transmission through solid channels. Transducers with a center frequency of 2.5 MHz are used to transmit and receive information-bearing ultrasonic waves in solid channels where the communication bit rate can reach up to 1.5 Mbps.
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Title: DATA-DRIVEN OPTIMIZATION OF NEXT GENERATION HIGH-DENSITY WIRELESS NETWORKS
Creator: Khairy, Sami
Date: 2021
Description: The Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and...
Show moreThe Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and computations. In the IoT era, an enormous number of devices will be connected wirelessly to the internet in order to enable advanced data-centric applications. The projected growth in the number of connected wireless devices poses new challenges to the design and optimization of future wireless networks. For a wireless network to support a massive number of devices, advanced physical layer and channel access techniques should be designed, and high-dimensional decision variables should be optimized to manage network resources. However, the increased network scale, complexity, and heterogeneity, render the network unamenable to traditional closed-form mathematical analysis and optimization, which makes future high-density wireless networks seem unmanageable. In this thesis, we study the design and data-driven optimization of future high-density wireless networks operating over the unlicensed band, including Radio Frequency (RF)-powered wireless networks, solar-powered Unmanned Aerial Vehicle (UAV)-based wireless networks, and random Non-Orthogonal Multiple Access (NOMA) wireless networks. For each networking scenario, we first analyze network dynamics and identify performance trade-offs. Next, we design adaptive network controllers in the form of high-dimensional multi-objective optimization problems which exploit the heterogeneity in users' wireless propagation channels and energy harvesting to maximize the network capacity, manage battery energy resources, and achieve good user capacity fairness. To solve the high-dimensional optimization problems and learn the optimal network control policy, we propose novel, cross-layer, scalable, model-based and model-free data-driven network optimization and resource management algorithms that integrate domain-specific analyses with advanced machine learning techniques from deep learning, reinforcement learning, and uncertainty quantification. Furthermore, convergence of the proposed algorithms to the optimal solution is theoretically analyzed using mathematical results from metric spaces, convex optimization, and game theory. Finally, extensive simulations have been conducted to demonstrate the efficacy and superiority of our network optimization and resource management techniques compared with existing methods. Our research contributions provide practical insights for the design and data-driven optimization of next generation high-density wireless networks.
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Title: Efficient Power System Transient Simulation for Stability Studies Based on Frequency Response Optimized Approximation
Creator: Lei, Sheng
Date: 2021
Description: Power systems world-wide are going through a paradigm change with dramatically increasing power electronics integration and more emphasis on...
Show morePower systems world-wide are going through a paradigm change with dramatically increasing power electronics integration and more emphasis on the intrinsically unbalanced distribution side. The new features of power systems violate the fundamental assumptions and challenge the feasibility of transient stability simulation, a traditional tool for stability studies. Electromagnetic transient simulation is applicable to power systems with the new features, but its computational efficiency is too low with the typical microsecond-level step sizes.This dissertation aims at enabling millisecond-level step sizes, typically used in traditional transient stability simulation, in efficient electromagnetic transient simulation for system-level stability studies on unbalanced power systems, while assuring satisfactory accuracy. The approach taken is to introduce novel highly accurate numerical methods into electromagnetic transient simulation.Several implicit one-step frequency response optimized integrators considering second order derivative are proposed. Some existing numerical integrators in the literature of this category are reviewed. Their numerical properties are studied. Some of these numerical integrators are especially suitable to be used as numerical differentiators.A novel power system transient simulation scheme is put forward using the implicit one-step frequency response optimized integrators as the main numerical integrators and differentiators. Large step sizes are applicable with the proposed simulation scheme to achieve efficient electromagnetic transient simulation without sacrificing accuracy. Execution of the proposed simulation scheme is detailed.Several explicit and implicit multistep frequency response optimized integrators considering first or second order derivative are proposed. Some existing numerical integrators of these types are reviewed from the error frequency response viewpoint. Based on these numerical integrators, a prediction method is put forward to further accelerate the proposed simulation scheme without impacting its accuracy.Initialization process of the proposed simulation scheme is put forward. The initialization process calculates the periodic steady state solution of unbalanced power systems considering power flow conditions. The requirements of power system stability studies on the initial conditions for transient simulation runs are thus satisfied. Effectiveness and efficiency of the initialization process are demonstrated.Computational models of power system network elements in the proposed simulation scheme are detailed. The extended nodal analysis is put forward for the proposed simulation scheme to organize the computational models of most network elements in an efficient and elegant manner.Some power system devices are implemented with the proposed simulation scheme, including single-phase grid-feeding converter system, three-phase grid-feeding converter system, three-phase synchronous machine and three-phase induction machine. The proposed simulation scheme is shown to simultaneously achieve efficiency and accuracy as applied to these devices.The proposed simulation scheme is applied to different types of power systems, including transmission system, distribution system and combined transmission and distribution system. Its versatility is revealed. Its efficiency and accuracy are demonstrated with numerical case studies as applied to these systems.
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Title: Electronically Assisted Direct Current Circuit Breakers
Creator: Feng, Yanjun
Date: 2019
Description: DC power is gaining tractions recently, however, DC fault protection remains a major technical challenge. Popular and cost-effective AC...
Show moreDC power is gaining tractions recently, however, DC fault protection remains a major technical challenge. Popular and cost-effective AC mechanical circuit breakers do not offer sufficient DC interruption capability. Solid state circuit breakers have drawbacks of high cost and high conduction loss. The reported hybrid circuit breakers solutions require fast responding current sensors and mechanical actuation mechanism vastly different from and far more complex than the conventional AC circuit breakers.This thesis introduces a new DC hybrid circuit breaker concept termed Electronically Assisted Circuit Breaker (EACB). A conventional AC mechanical circuit breaker (MCB) is used to interrupt DC fault currents with the assistance of an electronic commutation circuit, which is activated for a short time period only during the late phase of the breaking process. Unlike other prior art HCB concepts, an EACB uses (1) a conventional thermal-magnetic AC baseline breaker design with minimal modification; and (2) an electronic commutation circuit which only needs to commutate a fault current already reduced from its peak for a very short duration (~100µs), both contributing to significant cost savings. While an EACB does not facilitate arc-free or ultrafast breaking, it does provide a simple and cost-effective way to enhance the DC current interruption capability of conventional thermal-magnetic AC circuit breakers currently dominating the low voltage circuit breaker market. The EACB concept has been validated both experimentally and by simulation. A 600VDC/250A (nominal) EACB prototype is designed and tested. It has experimentally demonstrated a fault current interruption capability of over 8kA at a DC voltage of 600V within 6 milliseconds.
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Title: TRANSIENT STABILITY SIMULATION OF COMBINED THREE-PHASE UNBALANCED TRANSMISSION AND DISTRIBUTION NETWORKS
Creator: Alsharief, Yagoob
Date: 2019
Description: Historically, transmission (T) system and distribution (D) system analysis has been done separately. The main reasons are 1) different...
Show moreHistorically, transmission (T) system and distribution (D) system analysis has been done separately. The main reasons are 1) different modeling frameworks, i.e., positive-sequence versus three-phase unbalanced, 2) system size, and 3) lack of dynamic two-way interaction between T&D. The typical power system usually consists of tens of thousands of transmission buses and thousands of distribution feeders with hundreds of customers per feeder. In the past, distribution networks have been largely passive with relatively little dynamic interaction with the transmission network. However, due to the new trends that the electric grid has been witnessing in the last decade with the installation of distributed energy resources (DERs) on the distribution level, such as behind-the-meter generation and energy storage units, electric vehicles, etc., dynamic simulation tools for combined T&D will become necessary in the near future. These tools will aid system operators and planning engineers in understanding the impact of these new trends on large-scale power systems. Taking advantage of the advancements in the field of high performance computing and parallel computing could enable accurate, wide-area T&D dynamics simulation. These comprehensive simulation capabilities would dramatically improve our ability to predict the complex interactions among DERs, customer loads and traditional utility control devices, thereby allowing higher penetrations of renewable energy, electric vehicles and energy storage.
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Title: LOW-DOSE CARDIAC SPECT USING POST-FILTERING, DEEP LEARNING, AND MOTION CORRECTION
Creator: Song, Chao
Date: 2019
Description: Single photon emission computed tomography (SPECT) is an important technique in use today for the detection and evaluation of coronary artery...
Show moreSingle photon emission computed tomography (SPECT) is an important technique in use today for the detection and evaluation of coronary artery diseases. The image quality in cardiac SPECT can be adversely affected by cardiac motion and respiratory motion, both of which can lead to motion blur and non-uniform heart wall. In this thesis, we mainly investigate imaging de-noising algorithms and motion correction methods for improving the image quality in cardiac SPECT on both standard dose and reduced dose.First, we investigate a spatiotemporal post-processing approach based on a non-local means (NLM) filter for suppressing the noise in cardiac-gated SPECT images. Since in recent years low-dose studies have gained increased attention in cardiac SPECT owing to its potential radiation risk, to further improve the image quality on reduced dose, we investigate a novel de-noising method for low-dose cardiac-gated SPECT by using a three dimensional residual convolutional neural network (CNN). Furthermore, to reduce the negative effect of respiratory-binned acquisitions and assess the benefit of this approach in both standard dose and reduced dose using simulated acquisitions. Inspired by the success in respiratory correction, we investigate the potential benefit of cardiac motion correction for improving the detectability of perfusion defects. Finally, to combine the benefit of above two types of motion correction, dual-gated data acquisitions are implemented, wherein the acquired list-mode data are further binned into a number of intervals within cardiac and respiratory cycle according to the electrocardiography (ECG) signal and amplitude of the respiratory motion.
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Title: Multi-function multi-modality sensing and communication system: a designer's perspective
Creator: Fepeussi, Tonmo Vanessa Carine
Date: 2019
Description: The combination of sensing and communication functionalities on the same electronic device is the key to autonomous sensing applications in...
Show moreThe combination of sensing and communication functionalities on the same electronic device is the key to autonomous sensing applications in the transportation industry, including driverless vehicles and structural health monitoring (SHM) of aero-vehicles. Due to the limited availability of spectral and hardware resources, there is a need for resource sharing between sensing and communication systems. This is achieved by the efficient integration of sensing and communication functions through a unified design of both systems into smart sensors. To that end, a multi-modality approach is employed in this research to design multi-functional systems at two different bands of the frequency spectrum, namely radio and acoustic frequencies.First, a radio-frequency (RF) software-defined system capable to support radar sensing and RF communication is proposed for use in modern interconnected automotive applications such as driverless vehicles. The proposed RF radar is designed on a software-defined homodyne transceiver prototype capable of radio communication. The system is implemented in the S band over a narrow frequency bandwidth of 34 MHz between 3.550 GHz and 3.584 GHz. Experimental measurements show that the designed radar sensor can measure short-range targets with a range accuracy of less than 21 cm.An acoustic sensing and communication system is developed in parallel for use in autonomous SHM monitoring of aero-vehicles. The proposed communication system uses M-ary time-reversal pulse position modulation (M-TRPPM) as the modulation scheme for dispersion compensated wireless communication across the elastic channel. The time reversal based time division multiple access (TR-TDMA) protocol is introduced to regulate channel access by multiple sensors. Simulation and experimental validation demonstrate that the designed system, using an excitation signal generated by a PZT sensor disc at 300 kHz resonant frequency, is capable of reliable data transmission with a bit error rate (BER) approximating zero at low data rates of a few kilobits per seconds.
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Title: Integrated Design Framework For Electric Motor Drive Systems
Creator: Salameh, Mohamad
Date: 2020
Description: This thesis aims to develop a flexible and time-efficient framework for machine design optimization that considers driving cycles,...
Show moreThis thesis aims to develop a flexible and time-efficient framework for machine design optimization that considers driving cycles, multiphysics domains and current design. The proposed development of the framework is based on the enhancement of three key aspects in the machine design process. A data mining algorithm – the X-means – is employed in the driving cycle analysis, to establish a trade-off between the optimization objectives and the computational intensity. A novel vibration surrogate model is proposed to evaluate the vibroacoustic behavior of the machine in an accurate and time-efficient way. In the identification process, the time effectiveness of the model is attained with a minimized number of finite element simulations. Furthermore, the principle of simultaneous coupled optimization is considered in the framework, where current design variables are included in the optimization environment to allow identifying design candidates with improved performance.
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Title: ANALYSIS AND OPTIMIZATION OF VIBRATION AND ACOUSTIC NOISE IN SWITCHED RELUCTANCE MACHINES
Creator: Yaman, Selin
Date: 2019
Description: One of the main drawbacks of switched reluctance machines (SRM) is the vibration and high acoustic noise compared to other electrical motors....
Show moreOne of the main drawbacks of switched reluctance machines (SRM) is the vibration and high acoustic noise compared to other electrical motors. The root cause of the high level of acoustic noise is radial forces with high harmonic content. These harmonics may trigger resonant modes in the stator and cause the machine to create high vibration and acoustic noise. To better understand the factors influencing vibration and acoustic noise in an SRM, this dissertation first develops a multi-physics model in ANSYS Workbench environment and carries out a comprehensive analysis of multiple variations in stator and rotor geometries. Based on this understanding, this dissertation identifies distinct factors affecting noise in the machine, which are affected by electromagnetic design and power electronic control. From the electromagnetic perspective, geometrical optimizations in the stator and rotor structures are evaluated to understand the impact on NVH (noise, vibration and harshness) performance. This background is used to develop a fast geometry-sensitive analytical approach to reduce acoustic noise in the machine. While optimizing the geometry for a silent machine design, different design of experiments (DoE) methods and response surface (RS) optimization methods are also compared and presented. Furthermore, material analysis is included in structural design, where high flux material effect on vibration and acoustic noise is observed. The second topic of the NVH analysis is power electronic and switching solutions. In this study, multiple basic and advanced switching techniques have been considered and optimized to reduce acoustic noise under a preset efficiency constraint. Further, a time efficient model of SRM is introduced with vibro-acoustic noise perspective by developing a computationally cost effective SRM modeling. By using this analytical time-efficient NVH model, a current shape optimization is implemented, and results are discussed. Finally, experimental validations are provided for NVH and psychoacoustics analysis for different operating conditions and current control methods.
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Title: BLOCKCHAIN FOR TRANSACTIVE ENERGY MARKET WITH NETWORKED MICROGRIDS
Creator: Yan, Mingyu
Date: 2021
Description: Transactive energy, which allows and incentivizes microgrids (MGs) to trade energy with each other, is regarded as the next-generation energy...
Show moreTransactive energy, which allows and incentivizes microgrids (MGs) to trade energy with each other, is regarded as the next-generation energy management scheme to accommodate the penetration of distributed energy resources (DERs). Blockchain provides an effective and decentralized strategy, which can address the operational challenges introduced by the transactive energy market. This thesis is aimed at providing effective transactive energy markets for incentivizing MGs to trade energy and utilizing blockchain technologies to provide a secure and efficient energy trading environment for all participants.First, this thesis offers a centralized transactive market for networked MGs to transact energy through the centralized distribution system operator (DSO) while ensuring the power network limits. All MGs cooperate in this market and the cooperative behaviors are captured using the cooperative game with externalities. A two-level problem is studied to allocate the total payoff to all participating MGs. Numerical results for a 4-MG system and the IEEE 33-bus show the validity of the centralized transactive energy model. Second, this thesis proposes a two-level network-constrained peer-to-peer (P2P) transactive energy for multi-MGs, which guarantees the distribution power network security and allows MGs to trade energy with each other flexibly. At the lower level, a P2P transactive energy is employed for multi-MGs to trade energy with each other. A multi-leader multi-follower (MLMF) Stackelberg game approach is utilized to model the energy trading process among MGs. At the upper level, the DSO reconfigures the distribution network based on the P2P transactive energy trading results by applying the AC optimal power flow considering the distribution network reconfiguration. If there are any network violations, the DSO requests energy trading adjustments at the lower level for network security. Numerical results for a 4-MG system, the modified IEEE 33-bus, and the 123-bus distribution power systems show the effectiveness of the proposed transactive energy model and its solution technique. Third, this thesis adopts the blockchain for the peer-to-peer transactive energy market among MGs. A two-level integrated blockchain-power system is provided, in which all MGs and the DSO are equipped with blockchain. At the lower level, MGs trade energy with each other through the lower-level MG blockchain, while the DSO manages the network security through the upper level DSO blockchain. We illustrate how to utilize blockchain technologies, i.e., public and private keys and smart contracts, to provide an efficient and secure energy trading environment for all MGs. Last, this thesis applies the blockchain for transacting energy and carbon right for networked MGs. MGs transact energy and carbon right through the centralized DSO while ensuring the power network limits. The introduction of blockchain achieves secure and decentralized market settlements in this centralized market. Numerical results for a 4-MG system and modified IEEE 33-bus systems show the effectiveness of the proposed transactive energy and carbon market.
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Title: IMPROVING DEEP LEARNING BASED SEMANTIC SEGMENTATION USING CONTEXT INFORMATION
Creator: Xia, Zhengyu
Date: 2021
Description: Semantic segmentation is an important but challenging task in computer vision because it aims to assign each pixel a category label accurately...
Show moreSemantic segmentation is an important but challenging task in computer vision because it aims to assign each pixel a category label accurately. Nowadays, applications such as autonomous driving, path navigation, image search engine, or augmented reality require accurate semantic analysis and efficient segmentation mechanisms. In this thesis, we propose multiple models to improve the performance of semantic segmentation. In the first part, we focus on the single-task network, which aims to improve the performance of semantic segmentation. Our research includes exploiting context information using mixed spatial pyramid pooling to extract dense context-embedded features in FCN-based semantic segmentation. We also propose a GAF module to generate a global context-based attention map to guide the shallow-layer feature maps for better pixel localization. In the second part, we focus on a multi-task network that incorporates semantic segmentation to improve other computer vision tasks such as object detection. Specifically, a multi-task network, along with a learning strategy is designed to let semantic segmentation and object detection assist each other since they are highly correlated. Also, we include weakly-supervised multi-label semantic segmentation learning to deal with the shortage of high-quality training examples and to improve the performance of cross-domain object detection. In the third part, we focus on improving the performance of video panoptic segmentation, which is a unified network that incorporates semantic segmentation and instance segmentation using video streams. We design a new ConvLSTM pyramid to transmit spatio-temporal contextual information in our video panoptic segmentation network. Specifically, we propose a modified ConvLSTM to generate temporal contextual information. Also, we design an MSTPP module to obtain mixed spatio-temporal context-embedded feature maps. Experimental results on different datasets show that our proposed method achieves better performance compared with the state-of-the-art methods.
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