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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: Performance Analysis of Energy Harvesting- Non-Orthogonal Multiple Access IoT Network
Creator: Ni, Zhou
Date: 2019
Description: Internet of Things (IoT) systems in general consist of a lot of devices with massive connectivity. Those devices are usually constrained with...
Show moreInternet of Things (IoT) systems in general consist of a lot of devices with massive connectivity. Those devices are usually constrained with limited energy supply and can only operate at low power and low rate. One solution to limited energy is to use energy harvesting to provide sustainable energy. The set of technologies adopted in next-generation wireless communication systems, such as massive MIMO and Non-Orthogonal Multiple Access (NOMA), can provide solutions to increase the throughput of IoT systems. In this thesis, we investigate a cellular-based IoT system combined with energy harvesting and NOMA. We consider all base stations (BS) and IoT devices follow the Poisson Point Process (PPP) distribution in a given area. The unit time slot is divided into two phases, energy harvesting phase in downlink (DL) and data transmission phase in uplink (UL). That is, IoT devices will first harvest energy from all BS transmissions and then use the harvested energy to do the NOMA information transmission. We define an energy harvesting circle within which all IoT devices can harvest enough energy for NOMA transmission. The design objective is to maximize the total throughput in UL within the circle by varying the duration T of energy harvesting phase. In our work, we also consider the inter-cell interference in the throughput calculation. The analysis of Probability Mass Function (PMF) for IoT devices in the energy harvesting circle is also compared with simulation results. It is shown that the BS density needs to be carefully set so that the IoT devices in the energy harvesting circle receive relatively smaller interference and energy circles overlap only with small probability. Our simulations show that there exists an optimal T to achieve the maximum throughput. When the BSs are densely deployed consequently the total throughput will decrease because of the interference.
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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: Information Security Analysis of Modern Wireless Printers
Creator: Mehta, Keval Samirbhai
Date: 2019
Description: In today’s world, everything is becoming wireless. User-friendliness and security have always been on two opposite sides of the old-fashioned...
Show moreIn today’s world, everything is becoming wireless. User-friendliness and security have always been on two opposite sides of the old-fashioned scale when you give priority to one the second will get hit on somewhat level. This paper concentrates on the same thing in the case of printers. As wireless technology has been made available in the printers and they have got cheaper in recent time, the numbers of households owning the printers have increased dramatically in recent years. New printers use Wi-Fi direct or Wi-Fi AP technology to give wireless access to the user. Wi-Fi P2P also uses the same 802.11 protocol as Wi-Fi AP to help the user to print wirelessly; by directly connecting to or by directly sending commands and documents to the printers. We use a printer to print and scan important documents, which makes it a necessity, that the whole thing is secured. In this paper, I have tried to do analysis on possible security issues with wireless printers with the only wireless connection. The tests include the case where the bad guy will try to prevent the user to use the printer (DoS), from a distance and the case where the bad guy will try to sniff the packets or say important documents that the user is trying to print. I have tried to include different printers like HP, Brother, Canon to do testing, to get the overall idea of security in wireless printers. The first part includes the way of authentication available and the protocols used by the printers and the second part includes the possible ways to get bypass the security and recreate the printing materials that were printed by the user.
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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: Leakage Power Attack-Resilient Designs of A SRAM Cell in 7nm FinFET Technology
Creator: Chen, Kangqi
Date: 2019
Description: Recently, the classic metal-oxide-semiconductor field-effect-transistor (MOS- FET) has reached its limit for scaling. Another transistor...
Show moreRecently, the classic metal-oxide-semiconductor field-effect-transistor (MOS- FET) has reached its limit for scaling. Another transistor structure, FinFET, gradually has become the alternative choice for next generation of integrated circuits. Excellent features like reduced short channel effects, low threshold-voltage variability, less random dopant fluctuation, etc, offer this transistor model more stability, less leakage and faster performance. In particular, scaling trends force SRAM cells to be more vulnerable while using conventional MOSFET. The application of FinFET helps SRAM cell designs to overcome stability issues and achieve less power and faster speed. Another critical feature of an SRAM cell that needs to be considered is the correlation between data stored in cell and leakage of this cell. Side-Channel Attacks (SCA) like Leakage Power Analysis (LPA) would exploit this correlation to decrypt the secret key inside the memory. SCA has been proved to be a non-invasive but dangerous threat. Therefore, LPA would be the main focus of this thesis research.In this thesis, firstly, threshold voltage of various models are investigated using fundamental logic circuits including full-adders built with pass transistors, CLRCL and SERF. Secondly, conventional 6T SRAM cell design and single-ended 9T SRAM cell design targeting high stability and low power, are implemented and compared. Thirdly, the leakage balance method is applied to 9T cell design. Two novel solutions for LPA prevention of 9T design are proposed, implemented and compared against the original 9T design and conventional 6T design. The results confirm improved leakage balance and attack resilience while maintaining the stability and low-power features of the original 9T SRAM cell.
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Title: DAMAGE ASSESSMENT OF CIVIL STRUCTURES AFTER NATURAL DISASTERS USING DEEP LEARNING AND SATELLITE IMAGERY
Creator: Jones, Scott F
Date: 2019
Description: Since 1980, millions of people have been harmed by natural disasters that have cost communities across the world over three trillion dollars....
Show moreSince 1980, millions of people have been harmed by natural disasters that have cost communities across the world over three trillion dollars. After a natural disaster has occurred, the creation of maps that identify the damage to buildings and infrastructure is imperative. Currently, many organizations perform this task manually, using pre- and post-disaster images and well-trained professionals to determine the degree and extent of damage. This manual task can take days to complete. I propose to do this task automatically using post-disaster satellite imagery. I use a pre-trained neural network, SegNet, and replaced its last layer with a simple damage classification scheme. This final layer of the network is re-trained using cropped segments of the satellite image of the disaster. The data were obtained from a publicly accessible source, the Copernicus EMS system. They provided three channel (RGB) reference and damage grading maps that were used to create the images of the ground truth and the damaged terrain. I then retrained the final layer of the network to identify civil structures that had been damaged. The resulting network was 85% accurate at labelling the pixels in an image of the disaster from typhoon Haiyan. The test results show that it is possible to create these maps quickly and efficiently.
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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: A Complete Machine Learning Approach for Predicting Lithium-Ion Cell Combustion
Creator: Almagro Yravedra, Fernando
Date: 2020
Description: The object of the herein thesis work document is to develop a functional predictive model, able to predict the combustion of a US18650 Sony...
Show moreThe object of the herein thesis work document is to develop a functional predictive model, able to predict the combustion of a US18650 Sony Lithium-Ion cell given its current and previous states. In order to build the model, a realistic electro-thermal model of the cell under study is developed in Matlab Simulink, being used to recreate the cell's behavior under a set of real operating conditions. The data generated by the electro-thermal model is used to train a recurrent neural network, which returns the chance of future combustion of the US18650 Sony Lithium-Ion cell. Independently obtained data is used to test and validate the developed recurrent neural network using advanced metrics.
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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: GAME THEORY BASED LOCATION-AWARE CHARGING SOLUTIONS FOR NETWORKED ELECTRIC VEHICLES
Creator: Laha, Aurobinda
Date: 2020
Description: The recent explosive adoption of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) has sparked considerable interest of...
Show moreThe recent explosive adoption of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) has sparked considerable interest of academia in developing efficient charging schemes. Supported by the advanced vehicle-to-grid (V2G) network, vehicles and charging stations can respectively make better charging and pricing decisions via real-time information sharing. In this research, we study the charging problem in an intelligent transportation system (ITS), which consists of smart-grid enabled charging stations and networked EVs. Each vehicle aims to select a station with the lowest charging cost by considering the charging prices and its location while the objective of a charging station is to maximize its revenue given the charging strategy of the vehicles. We employ a multileader multi-follower Stackelberg game to model the interplay between the vehicles and charging stations, in which the location factor plays an important role. We show that there exists a unique equilibrium for the followers’ subgame played by the vehicles, while the stations are able to reach an equilibrium of their subgame with respect to the charging prices. Therefore, the Nash equilibrium of the Stackelberg game is achievable through the proposed charging scheme. We further evaluate the price of anarchy (PoA) of the proposed charging scheme by using a centralized optimization model, in which a modified matching algorithm is applied. In state-of-the-art research works, PHEVs tend to charge or discharge to a smart grid individually. In our extended work, we also consider the discharging scenarios for PHEVs, which is generally during the peak hours of a micro-grid system. We propose that by leveraging the cooperation between charging and discharging PHEVs, the grid will be able to properly disperse the charging load in the load valley and discharging during the load peak hours. As a consequence, the electricity load will be well balanced. In this process, the PHEVs also receive greater benefit, thus serving the PHEV charging and discharging cooperation as a win-win strategy for both the grid and the PHEV users. We formulate and resolve the PHEV charging and discharging cooperation in the framework of a coalition game. Finally, simulation results confirm the uniqueness of the equilibrium in both the game strategies. A performance comparison between the proposed distributed and centralized strategy with existing solutions are presented. We also provide the results of the coalition game when both charging and discharging PHEVs are present in the network. The proper management of charging and discharging of EVs poses one of the most challenging and interesting issues in our research. We aim to provide a complete demand response management solution to PHEVs and micro-grids in a real-time scenario.
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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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