Electrical and Computer Engineering Commons^™

Hybrid Quantum-Classical Optimization Of The Resource Scheduling Problem, Tyler Christeson, Md Habib Ullah, Ali Arabnya, Amin Khodaei, Rui Fan

Electrical and Computer Engineering: Faculty Scholarship

Resource scheduling is critical in many industries, especially in power systems where the Unit Commitment (UC) problem determines the on/off status and output levels of generators under physical and economic constraints. Traditional exact methods, such as Branch-and-Bound, Branch-and-Cut, dynamic programming and mixed-integer linear programming (MILP), remain the backbone of UC solution techniques, but they often rely on linear approximations or exhaustive search, leading to high computational burdens as system size grows. Metaheuristic approaches, such as genetic algorithms, particle swarm optimization, and other evolutionary methods, have been explored to mitigate this complexity; however, they typically lack optimality guarantees, exhibit sensitivity to …

High Frequency Dc-Dc Converter Design Optimization, Modeling And Control Based-Wbg Technology., Salah Ahmed Abdullah Eltief

Electronic Theses and Dissertations

The performance of DC-DC power converters is a cornerstone of modern electric vehicle (EV) powertrains, directly governing overall system efficiency, size, cost, and reliability. This dissertation presents a comprehensive performance analysis and optimization of DC-DC converter topologies to determine the most suitable design for high voltage EV applications. The evaluation rigorously compares efficiency, power losses, and physical size under a range of harsh operating conditions. A primary objective is to leverage Wide Bandgap (WBG) semiconductors, specifically Silicon Carbide (SiC), to push the performance boundaries of power conversion. While SiC devices are known for their superior material properties, a clear understanding …

Capacity, Allocation And Update Dynamics Of Human Memory Systems, Shaoying Wang

Electronic Theses and Dissertations

Information is encoded and stored in three types of memory: sensory memory (SM), short-term memory (STM), and long-term memory (LTM). SM has a large capacity but retains information for only a brief period. When information transfers to STM, only a limited amount can be stored. Information in STM can then be transferred to LTM, which has a much larger capacity and longer retention time. STM is often conceptualized as working memory (WM) to highlight its role in active information processing. Due to the limited capacity of STM, it is commonly believed that STM serves as the bottleneck for information processing. …

Deep Reinforcement Learning Based Control For Enhanced Frequency Response With Multi-Energy Storage Systems, Abu Shouaib Hasan, Rui Fan, Wei Gao, Di Wu

Electrical and Computer Engineering: Faculty Scholarship

This paper proposes an advanced strategy for managing multiple battery energy storage systems (BESS) to enhance frequency support during contingencies. A novel deep reinforcement learning (DRL) framework based on a guided surrogate-gradient-based evolutionary strategy (GSES) was developed to dynamically regulate BESS outputs for rapid power injection or absorption. This approach effectively mitigates the rate of change of frequency (ROCOF) and stabilizes the system frequency under varying operating conditions. Parallel computing techniques are employed to accelerate training and ensure robust performance. In addition, a genetic algorithm is implemented to determine the placement of BESS within the grid network, strategically minimizing ROCOF …

Development Of An Embedded Iot Board For Real-Time Floor Estimation Of Autonomous Robots, Carter J. Sorensen

Electronic Theses and Dissertations

As service robots become more prevalent in multi-story environments such as hospitals, hotels, and laboratories, accurate floor-level detection is critical to ensuring operational reliability. Consider a robot tasked with delivering medical samples in a multi-story laboratory. Without accurate feedback, a robot exiting on the wrong floor could introduce delays, disrupt workflows, or compromise sample integrity. Internet of Things (IoT) technologies offer a way to address these risks by providing real-time error detection and corrective capability. However, current IoT-based floor estimation systems often require invasive modifications to building infrastructure—particularly elevator control panels. These approaches introduce challenges related to cost, liability, backward …

An Advanced Hexacopter For Autonomous Exploration Of Mars: Attitude Control And Navigation Strategies, Laura Sopegno

Electronic Theses and Dissertations

Mars exploration has recently witnessed major interest within the scientific community. Unmanned robotic platforms offer reliable solutions to acquire and collect data and information from the Red Planet. Particularly, rovers, landers, and orbiters have significantly shaped planetary exploration on the Moon and Mars, contributing significantly to past missions while also highlighting limitations in their capacity to cover diverse terrains over wide ranges. Given current advances in Unmanned Aircraft Systems (UASs), Unmanned Aerial Vehicles (UAVs) offer promising alternatives for future scientific missions.

It is argued that hexacopters, with their relatively compact design and redundancy, present a promising …

Model-Based Navigation And Control Of Multirotor Uavs: A Machine Learning Approach, Serhat Sönmez

Electronic Theses and Dissertations

In recent decades, unmanned systems, particularly Unmanned Aerial Vehicles (UAVs), have seen significant advancement and unprecedented growth in military, civilian and public domain applications. Scientists have focused on enhancing UAV navigation and control through cutting-edge technologies and support tools. UAVs find applications in many fields, except military, such as agriculture, infrastructure inspection, wildlife monitoring, search and rescue, emergency response, border protection, to name but a few relevant civilian applications. Given the faster-than-exponential increase of available computational power, learning-based algorithms have emerged as a prominent tool for (real-time) multirotor UAV navigation and control. This dissertation centers around the fusion of conventional …

Koopman-Based Modeling For Nonlinear Control Of Multirotor Uavs, Simone Martini

Electronic Theses and Dissertations

This PhD dissertation focuses on adopting the emerging Koopman Operator theory for modeling and nonlinear control of multirotor UAVs, focusing specifically on quadrotors for proof-of-concept demonstration purposes.

The Koopman Operator theory is based on the foundation that nonlinear dynamics in the state space may be represented as a linear evolution of some functions in the state space. Thus, using appropriately defined and possibly nonlinear functions of the state variables, called observables, as a new and maybe infinite set of coordinates that are referred to as lifted space, the original nonlinear dynamics appear to be linear. The implications of this theory …

Incremental Quantities Based Permissive Overreaching Transfer Trip Scheme For Protecting Inverter-Based Renewable Resources, Osama Zangoti

Electronic Theses and Dissertations

The power generation landscape evolves, with the increase of inverter-based resources (IBRs) such as solar photovoltaics and wind turbines, providing sustainable and clean energy sources. The shift towards IBRs mitigates climate change, creating considerable challenges to traditional power system protection due to their low fault current. Conventional protection schemes are designed around the internal dynamic of synchronous generators where they can supply an elevated fault current. This thesis explores a protection scheme designed to enhance the security of IBRs. The incremental characteristics of voltage and current coupled with the Permissive Overreaching Transfer Trip scheme (POTT) provide a remarkable ability to …

Quantum-Powered Battery Scheduling In Modern Distribution Grids, Diba Ehsani

Electronic Theses and Dissertations

The rising need for exploiting a novel and evolved computation is an increasing concern in the power distribution system to address the exponential growth of distribution-connected devices. Scheduling numerous battery energy storage systems in an optimal way is one of the emerging challenges that will be more noticeable as the number of batteries, including residential, community, and vehicle batteries, increases in the grid. This thesis focuses on this topic and offers a necessary component in building the quantum-compatible distribution system of the future. Using a constrained quadratic model (CQM) on D-Wave’s hybrid solver as well as a binary quadratic model …

Data-Driven Approaches For Enhancing Power Grid Reliability, Behrouz Sohrabi

Electronic Theses and Dissertations

This thesis explores the transformative potential of data-driven approaches in addressing key operational and reliability issues in power systems. The first part of this thesis addresses a prevalent problem in power distribution networks: the accurate identification of load phases. This study develops a data-driven model leveraging consumption measurements from smart meters and corresponding substation data to reconstruct topology information in low-voltage distribution networks. The proposed model is extensively tested using a dataset with more than 5,000 real load profiles, demonstrating satisfactory performance for large-scale networks. The second part of the thesis pivots to a crucial safety concern: the risk and …

Molecular Dynamics Study Of Characterization In Metal-Free Friction Materials, Yizhan Zhang

Electronic Theses and Dissertations

Metallic friction materials currently used in industry may adversely impact the environment. Substitutions for metals in friction materials, on the other hand, can introduce operational safety issues and other unforeseeable issues such as thermal-mechanical instabilities and insufficient strength. In view of it, this dissertation focuses on developing different kinds of materials from simple structure to complex structure and evaluating the material properties with the assistance of molecular dynamics (MD) tools at the nano scale.

First, the concept of the contacted surfaces in friction at the atomic scale was introduced in order to get accurate understanding of the friction process compared …

Frequency Stability Constrained Grid Operation With High Penetration Of Renewables, Ningchao Gao

Electronic Theses and Dissertations

Achieving carbon neutrality necessitates a significant integration of renewable energy into power systems. However, the swift implementation of fluctuating renewable energy (VRE) sources such as solar photovoltaics (PV) exacerbates real time power imbalances due to the randomness and uncertainty associated with VRE power generation. Further, the declining reliance on conventional synchronous generators (SGs) for system inertia presents considerable challenges in maintaining frequency stability, especially following disturbances.

Two possible solutions are proposed to address the first issue. Firstly, a speedy real time generation dispatch can be scheduled to allocate generation resources within short time intervals to effectively respond to changes in …

Consensus-Based Active And Reactive Power Control And Management Of Microgrids, Shruti Singh

Electronic Theses and Dissertations

Microgrids incorporating distributed generation and renewable energy sources offer potential solutions to the energy crisis while modernizing traditional grids. Despite cost-effectiveness in some technologies, financial support remains crucial for expensive ones like PV, fuel cells, and storage technologies. Microgrids bring economic benefits, efficiency, reduced emissions, and improved power quality. Their success hinges on cost reductions in renewables, storage, reliability, and energy management systems, enabling operation both with and without the utility grid.

Economic Dispatch optimizes system costs, considering all constraints. Various methods tackle this problem, including quadratic convex functions, Lagrangian relaxation, and quadratic programming. For microgrids with distributed generators, seamless …

The Modernization Of Large Power Transformer Tanks, Babajide O. Williams

Electronic Theses and Dissertations

Due to the current demands placed on the power grid in terms of climate change, increasing urbanization, and terrorist attacks, the U.S. government in response to these demands, mandated that all the grid components be modernized in order to increase their reliability. As a critical component of the grid, Large Power Transformers (LPTs) play a key role in ensuring sustainable power generation and distribution. A literature search performed in this work and the analysis of data retrieved from the search showed that the tanks of these LPTs are critical to their durability, longevity, and reliability. Therefore, the reliability of LPTs …

Design Of Hybrid Inverters Using Wideband Gap Semiconductors For Microgrid Application, Luca Gacy

Electronic Theses and Dissertations

As the world becomes more reliant on renewable energy sources such as solar and wind power, the need for high efficiency high power inverters connected to homes is more relevant than ever. Connecting these renewable energy sources (RES) coupled with an energy storage system (ESS) to the grid through a hybrid inverter, with the highest efficiency and grid stability, is quickly becoming a necessity for the near future. This thesis explores the integration of wide band gap semiconductors for the power stage in these systems, along with the analysis of hybrid inverter topologies and structures. The goal of this thesis …

Novel Approach For Non-Invasive Prediction Of Body Shape And Habitus, Emma Young

Electronic Theses and Dissertations

While marker-based motion capture remains the gold standard in measuring human movement, accuracy is influenced by soft-tissue artifacts, particularly for subjects with high body mass index (BMI) where markers are not placed close to the underlying bone. Obesity influences joint loads and motion patterns, and BMI may not be sufficient to capture the distribution of a subject’s weight or to differentiate differences between subjects. Subjects in need of a joint replacement are more likely to have mobility issues or pain, which prevents exercise. Obesity also increases the likelihood of needing a total joint replacement. Accurate movement data for subjects with …

Deep Learning For Power Flow Estimation And High Impedance Fault Detection, Kun Yang

Electronic Theses and Dissertations

My thesis is divided into two parts.

The first part is: “Optimal Power Flow Estimation Using One-Dimensional Convolutional Neural Network [1]“. Optimal power flow (OPF) is an important research topic in power system operation and control decisions. Traditional OPF problems are solved through dynamic optimization with nonlinear programming techniques. For a large power system with large amounts of variables and constraints, the solving process would take a long time. This paper presents a new method to quickly estimate the OPF results using a one-dimensional convolutional neural network (1D-CNN). The OPF problem is treated as a high-dimensional mapping between the load …

Unsupervised Learning Algorithm For Noise Suppression And Speech Enhancement Applications, Abdullah Zaini Alsheibi

Electronic Theses and Dissertations

Smart and intelligent devices are being integrated more and more into day-to-day life to perform a multitude of tasks. These tasks include, but are not limited to, job automation, smart utility management, etc., with the aim to improve quality of life and to make normal day-to-day chores as effortless as possible. These smart devices may or may not be connected to the internet to accomplish tasks. Additionally, human-machine interaction with such devices may be touch-screen based or based on voice commands. To understand and act upon received voice commands, these devices require to enhance and distinguish the (clean) speech signal …

Power System Dynamic Control And Performance Improvement Based On Reinforcement Learning, Wei Gao

Electronic Theses and Dissertations

This dissertation investigates the feasibility and effectiveness of using Reinforcement Learning (RL) techniques for power system dynamic control, particularly voltage and frequency control. The conventional control strategies used in power systems are complex and time-consuming due to the complicated high-order nonlinearities of the system. RL, which is a type of neural network-based technique, has shown promise in solving these complex problems by fitting any nonlinear system with the proper network structure.

The proposed RL algorithm, called Guided Surrogate Gradient-based Evolution Strategy (GSES) determines the weights of the policy (which generates the action for our control reference signal) without back-propagation process …

Artificial Emotional Intelligence In Socially Assistive Robots, Hojjat Abdollahi

Electronic Theses and Dissertations

Artificial Emotional Intelligence (AEI) bridges the gap between humans and machines by demonstrating empathy and affection towards each other. This is achieved by evaluating the emotional state of human users, adapting the machine’s behavior to them, and hence giving an appropriate response to those emotions. AEI is part of a larger field of studies called Affective Computing. Affective computing is the integration of artificial intelligence, psychology, robotics, biometrics, and many more fields of study. The main component in AEI and affective computing is emotion, and how we can utilize emotion to create a more natural and productive relationship between humans …

Optimal Management And Sizing For Battery Energy Storage Systems For Grid Applications, Abdullah Muslih Alharbi

Electronic Theses and Dissertations

Battery energy storage systems (BESSs) play a dominant role in the reliability, resiliency, economics, and operational flexibility of the power grid. As such, the BESS is a promising and reliable green technology for supporting power grid facilities. However, the integration of BESS technology into power projects can be expensive and challenging, both technologically and in terms of regulation compliance. Thus, it is necessary to examine best practices for fully optimizing BESS sizing, operation, and scheduling in the context of benefiting BESS operators and investors by fully exploiting the valuable economic and technical benefits of BESSs. In this research, we identify …

Behavior, Switching Losses, And Efficiency Enhancement Potentials Of 1200 V Sic Power Devices For Hard-Switched Power Converters, Ali Mahmoud Salman Al-Bayati, Mohammad Abdul Matin

Electrical and Computer Engineering: Faculty Scholarship

Semiconductor power devices are the major constituents of any power conversion system. These systems are faced by many circumscriptions due to the operating constraints of silicon (Si) based semiconductors under certain conditions. The emergence and persistence evolution of wide bandgap technology pledge to transcend the restrictions imposed by Si based semiconductors. This paper presents a thorough experimental study and assessment of the performance of three power devices: 1200 V SiC cascode, 1200 V SiC MOSFET, and 1200 V Si IGBT under the same hardware setup. The study aims to capture the major attributes for each power device toward determining their …

Ad-Corre: Adaptive Correlation-Based Loss For Facial Expression Recognition In The Wild, Ali Pourramezan Fard, Mohammad H. Mahoor

Electrical and Computer Engineering: Faculty Scholarship

Automated Facial Expression Recognition (FER) in the wild using deep neural networks is still challenging due to intra-class variations and inter-class similarities in facial images. Deep Metric Learning (DML) is among the widely used methods to deal with these issues by improving the discriminative power of the learned embedded features. This paper proposes an Adaptive Correlation (Ad-Corre) Loss to guide the network towards generating embedded feature vectors with high correlation for within-class samples and less correlation for between-class samples. Ad-Corre consists of 3 components called Feature Discriminator, Mean Discriminator, and Embedding Discriminator. We design the Feature Discriminator component to guide …

Data-Enabled Distribution Grid Management, Zohreh Sadat Hosseini

Electronic Theses and Dissertations

In 2020, U.S. electric utilities installed more than 94 million advanced meters, which brought the percentage of residential customers equipped with smart meters to 75%. This significant investment allows collecting extensive customer data at the distribution level, however, the data are not currently leveraged effectively to help with system operations. This dissertation aims to use the smart meters’ data to improve the grid’s reliability, stability, and controllability by solving two of the most challenging problems at the distribution level, namely distribution network phase identification and outage identification.

Distribution networks have typically been the least observable and most dynamic and locally …

Classification Of Electropherograms Using Machine Learning For Parkinson’S Disease, Soroush Dehghan

Electronic Theses and Dissertations

Parkinson’s disease (PD) is a neurodegenerative movement disorder that progresses gradually over time. The onset of symptoms in people who are suffering from PD can vary from case to case, and it depends on the progression of the disease in each patient. The PD symptoms gradually develop and exacerbate the patient’s movements throughout time. An early diagnosis of PD could improve the outcomes of treatments and could potentially delay the progression of this disorder and that makes discovering a new diagnostic method valuable. In this study, I investigate the feasibility of using a machine learning (ML) approach to classify PD …

Learning Approach For Fast Approximate Matrix Factorizations, Haiyan Yu

Electronic Theses and Dissertations

Efficiently computing an (approximate) orthonormal basis and low-rank approximation for the input data X plays a crucial role in data analysis. One of the most efficient algorithms for such tasks is the randomized algorithm, which proceeds by computing a projection XA with a random projection matrix A of much smaller size, and then computing the orthonormal basis as well as low-rank factorizations of the tall matrix XA. While a random matrix A is the de facto choice, in this work, we improve upon its performance by utilizing a learning approach to find an adaptive projection matrix A from a set …

Distributed Control, Optimization, And State Estimation For Renewable Power System, Qiao Li

Electronic Theses and Dissertations

The traditional power systems are usually centralized systems, in which the control, operation and monitoring are performed by the centralized control center, e.g., SCADA. However, with the development of renewable energy, power systems are getting more and more distributed. So, it becomes necessary to establish the distributed power system operation methods for these power systems. In this research, the distributed techniques for the renewable power systems are proposed based on the consensus protocol technique from graph theory. These techniques cover the three important problems in power systems, i.e., economic dispatch, state estimation, and optimal power flow. First, the Distributed Economic …

Design, Manufacture, And Test Of A Hybrid Aerial-Ground Robotic Platform, William Garrett Willmon

Electronic Theses and Dissertations

A hybrid aerial-ground robotic platform allows for enhanced functionality combining most of the operational profiles of an aerial and ground vehicle with applications to intelligence, surveillance, reconnaissance (ISR), infrastructure inspection, emergency response, photography, etc. Motivated by this challenge, we designed, developed, and tested a prototype hybrid aerial-ground robotic vehicle capable of guidance, navigation, and control in the air and on the ground. The thesis focus is on the system design. As such, at first, we designed and analyzed the mechanical component to ensure durability. We then designed the electrical component to reduce overall weight and maximize battery life. We developed …

Mechanisms Of Sensory Adaptation In The Primate Visual System, Boris Isaac Peñaloza Rojas

Electronic Theses and Dissertations

Under ecological conditions, the luminance impinging on the retina varies within a dynamic range of 220 dB. Stimulus contrast can also vary drastically within a scene, and eye movements leave little time for sampling luminance. In addition, the amount of information reaching our visual system far exceeds the brain’s information processing capacity. Given the limited dynamic range of its neurons and its limited capacity in processing visual information in real-time, the brain deploys both structural and functional solutions that work in tandem to adapt to the surroundings. In this work, employing visual psychophysics and computational neuroscience, we study the mechanisms …