Electrical and Computer Engineering Commons^™

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

Characterization, Analysis, And Application Of Wbg Power Devices For Future Power Conversion Systems, Ali Mahmoud Salman Al-Bayati

Electronic Theses and Dissertations

Semiconductor power devices are the most momentous constituents of any power converter system. Fast switching, compactness, high performance and efficiency, and high temperature operation are the exacting challenges experienced by conventional silicon (Si) power device based power converters in many applications. In this dissertation, the wide bandgap (WBG) power devices are studied and used to transcend the limitations imposed by the Si power devices. It mainly focuses on characterization and analysis of the behavior of WBG power devices as well as design and development of efficient, high performance, and reliable dc–dc power converters based on WBG technology. First, using computer …

Deep Learning Methods For Fingerprint-Based Indoor And Outdoor Positioning, Fahad Alhomayani

Electronic Theses and Dissertations

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. The contribution of this dissertation is fourfold:

First, a Convolutional Neural Network (CNN)-based method for …

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 …

Thermal Performance Of Algan/Gan Based Power Switching Devices For Transformerless Inverters, Mahesh B. Manandhar

Electronic Theses and Dissertations

Wide Bandgap (WBG) semiconductors like Silicon Carbide (SiC), Gallium Nitride (GaN) and Aluminum Gallium Nitride (AlGaN) have superior material properties as compared to Silicon (Si) like higher electrical breakdown voltages and bandgap energies as well as lower leakage currents as compared to Si which make them ideal to operate at higher voltage with lower thermal losses. These properties make WBG materials ideal for power devices like Vertical Double-diffused Metal Oxide Semiconductor Field Effect Transistors (VDMOSFETs). The use of digital prototyping through computer simulation increases the speed and flexibility of the design iterations while reducing the cost and time required for …

Wind Turbine Parameter Calibration Using Deep Learning Approaches, Rebecca Mccubbin

Electronic Theses and Dissertations

The inertia and damping coefficients are critical to understanding the workings of a wind turbine, especially when it is in a transient state. However, many manufacturers do not provide this information about their turbines, requiring people to estimate these values themselves. This research seeks to design a multilayer perceptron (MLP) that can accurately predict the inertia and damping coefficients using the power data from a turbine during a transient state. To do this, a model of a wind turbine was built in Matlab, and a simulation of a three-phase fault was used to collect realistic fault data to input into …

Optimal Sizing And Operation Of A Pumped Thermal Energy Storage System, Matthew Perez

Electronic Theses and Dissertations

Current trends in the modern grid are leading to the integration of energy storage technologies (ESSs), such as pumped thermal energy storage to help incorporate more variable renewable energy sources into the grid. This paper analyzes the operation of a pumped thermal energy storage (PTES) system under the grid services of energy arbitrage, regulation services, spinning and non-spinning reserve, resource adequacy, and a combination of them all. Each revenue stream is setup into an optimization problem and solved to find which revenue generating technique would generate the most revenue. The combined revenue stream was found to produce the most revenue …

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 …

Experimental Investigation Of Low-Voltage Silicon Carbide (Sic) Semiconductor Devices For Power Conversion Applications, Saleh Salem H. Alharbi

Electronic Theses and Dissertations

Enhancing the performance and efficiency of power converter systems requires fast-switching power devices with considerably low switching and conduction losses. Silicon (Si) semiconductor devices are the essential components in electronic converter designs, and their behaviors and switching characteristics determine the system’s overall performance and efficiency. These conventional Si devices are nearing to hit their physical and operational limits in meeting power converter requirements with respect to high temperature and large voltage conditions. However, silicon carbide (SiC) power devices enable greater converter efficiency and better power density, particularly under hard switching frequencies and high output voltages due to their outstanding material …

Experimental Evaluation Of Medium-Voltage Cascode Gallium Nitride (Gan) Devices For Bidirectional Dc–Dc Converters, Salah Salem H. Alharbi

Electronic Theses and Dissertations

As renewable energy sources, such as photovoltaic (PV) cells and wind turbines, are rapidly implemented in DC microgrids, energy storage systems play an increasingly significant role in ensuring uninterrupted power supply and in supporting the reliability and stability of microgrid operations. Power electronics, especially bidirectional DC–DC converters, are essential parts in distributed energy storage and alternative energy systems because of their grid synchronization, DC power management, and bidirectional power flow capabilities. While there is increasing demand for more efficient, compact, and reliable power converters in numerous applications, most existing power converters are hindered by traditional silicon (Si) based semiconductors, which …

Developing Machine Learning Algorithms For Behavior Recognition From Deep Brain Signals, Hosein Golshan Mojdehi

Electronic Theses and Dissertations

Parkinson’s disease (PD) is a neurodegenerative condition and movement disorder that appears with symptoms such as tremor, rigidity of muscles and slowness of movements. Deep brain stimulation (DBS) is an FDA-approved surgical therapy for essential tremor and PD. Despite the fact that DBS substantially alleviates the motor signs of PD, it can cause cognitive side effects and speech malfunction mainly due to the lack of adaptivity and optimality of the stimulation signal to the patients’ current state. A behavior-adapted closed-loop DBS system may reduce the side effects and power consumption by adjusting the stimulation parameters to patients’ need.

Behavior recognition …

Deep Siamese Neural Networks For Facial Expression Recognition In The Wild, Wassan Hayale

Electronic Theses and Dissertations

The variation of facial images in the wild conditions due to head pose, face illumination, and occlusion can significantly affect the Facial Expression Recognition (FER) performance. Moreover, between subject variation introduced by age, gender, ethnic backgrounds, and identity can also influence the FER performance. This Ph.D. dissertation presents a novel algorithm for end-to-end facial expression recognition, valence and arousal estimation, and visual object matching based on deep Siamese Neural Networks to handle the extreme variation that exists in a facial dataset. In our main Siamese Neural Networks for facial expression recognition, the first network represents the classification framework, where we …

Microgrid-Enabled Reactive Power Support To Enhance Grid Economics, Sarhan Hasan

Electronic Theses and Dissertations

Reactive power plays an essential role in voltage control and stability in electric power systems. Various Volt/VAR techniques are utilized in electric power systems to maintain the voltage profile within defined acceptable limits and accordingly provide reliability and stability. Reactive power has been commonly generated through large-scale synchronous generators or distributed capacitor banks to provide proper transmission and distribution level system management, however, reactive power can be further used as an effective means to reduce total system operation cost. In this dissertation, an optimal reactive power model is proposed to determine the optimal nodal reactive powers that result in the …

Deep Reinforcement Learning For The Optimization Of Building Energy Control And Management, Jun Hao

Electronic Theses and Dissertations

Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC).

We assume that each building in our campus is equipped with smart meter and communication system which is envisioned in …

Aggregated Der Management In Advanced Distribution Grids, Mohsen Mahoor

Electronic Theses and Dissertations

Evolution of modern power systems are more distinct in distribution grids, where the growing integration of microgrids as well as distributed energy resources (DERs), including renewable energy resources, electric vehicles (EVs), and energy storage, poses new challenges and opportunities to grid management and operation. Rapid growth of distribution automation as well as equipment monitoring technologies in the distribution grids further offer new opportunities for distribution asset management. The idea of aggregated DERs is proposed as a remedy to streamline management and operation of advanced distribution grids, as discussed under three subjects in this dissertation. The first subject matter focuses on …

Real-Time Detection Of Demand Manipulation Attacks On A Power Grid, Srinidhi Madabhushi

Electronic Theses and Dissertations

An increased usage in IoT devices across the globe has posed a threat to the power grid. When an attacker has access to multiple IoT devices within the same geographical location, they can possibly disrupt the power grid by regulating a botnet of high-wattage IoT devices. Based on the time and situation of the attack, an adversary needs access to a fixed number of IoT devices to synchronously switch on/off all of them, resulting in an imbalance between the supply and demand. When the frequency of the power generators drops below a threshold value, it can lead to the generators …