Power and Energy Commons^™

Fault Identification And Localization In Distribution Grids Based On An Attention-Hybrid Graph Neural Network, Xingjian Shan

Theses and Dissertations--Electrical and Computer Engineering

This thesis proposes a multi-task fault diagnosis framework for distribution systems based on Graph Convolutional Networks (GCN) and an enhanced Graph Attention Network (GATv2). By representing the power grid as a graph with electrical features and topological connections, the model simultaneously performs fault type classification and fault location prediction. The architecture incorporates residual connections, multi-head attention, and a Jumping Knowledge module to capture multi-scale structural patterns, while dynamic loss weighting ensures balanced task optimization under noise and sparsity. Experimental results on the IEEE 123-node test feeder demonstrate a fault classification accuracy of 96.43%, and fault localization accuracies of 84.64% (strict), …

Mathematical-Programming Modeling Of Power-Electronics-Based Microgrid Systems, Jack A. Robey

Theses and Dissertations--Electrical and Computer Engineering

The emergence of power-electronics-based microgrid systems is driven by the shift to cleaner energy, transportation electrification, renewable integration, grid modernization through smart grid advancements, and growing demand for energy-efficient solutions. For utilities, these systems present unique opportunities for enhancing grid resilience, improving load management, and enabling distributed energy resource integration. This work presents a modeling and simulation approach for microgrid systems that uses mathematical programming to represent power flow and capture the system dynamics. By solving an optimization problem at each time step, the method enables evaluation of power distribution and system performance under a range of operating conditions, without …

Lifecycle Carbon Footprint And Sustainability Evaluation Of Dram-Based Processing In Memory Computing Architectures, Samrat Pravin Patel

Theses and Dissertations--Electrical and Computer Engineering

The use of computing technologies has significantly enhanced several aspects of our day-to-day lives. But it has still revealed significant environmental concerns, primarily related to greenhouse gas emissions and energy consumption. Initially, the primary environmental problems associated with computing were energy consumption during device operation. However, with the rapid advancement of technology and increasing computational demands, attention has shifted towards the embodied carbon footprint. This term refers to the total greenhouse gas emissions throughout a product’s lifecycle from the extraction of raw materials to end-of-life processing. It has become increasingly significant in the context of manufacturing integrated circuits (ICs), such …

Scalable Systems And Devices For Wireless Charging Of Electric Vehicles, Donovin D. Lewis

Theses and Dissertations--Electrical and Computer Engineering

The rising adoption of electric vehicles creates new opportunities that are not possible with conventional gas-powered vehicles such as wireless charging of electric vehicles (EV). Wide-scale implementation of wireless charging could result in benefits unique to EVs such as operation without human intervention, improved charging accessibility, and even in-route wireless charging for charge-sustaining or extended driving range operation. As the technology is in the early stages of development, there are many open-ended challenges to tackle including but not limited to coil and systems cost, weight and size, stray field emissions in high-power, high-frequency operation, and dynamic wireless charging system design …

Analysis And Design Optimization Of Electric Machines With Field Intensifying Configuration, Ali Mohammadi

Theses and Dissertations--Electrical and Computer Engineering

The design and optimization of electric machines face increasing demands for efficiency, improved torque density, manufacturability, and effective utilization of materials. Meeting these demands is particularly vital in for example, electric vehicles (EVs) and renewable energy systems, where performance, reliability, and cost are critical. In this dissertation innovative field-intensifying electric machine configurations have been explored, emphasizing advanced topologies, computational modeling, and optimization techniques to advance the state of the art in electric machine design and analysis.

Electric machines with high torque density are essential for many low-speed direct-drive systems, such as wind turbines, in-wheel traction, and industrial automation. This dissertation …

Multi-Objective Design Optimization Of Power Converters For Electric Aircraft Propulsion, Ben Luckett

Theses and Dissertations--Electrical and Computer Engineering

As global focus shifts to the electrification of the aviation sector, the need for high efficiency, lightweight, and reliable electric aircraft propulsion power converter systems has become apparent. These goals can be somewhat conflicting with each other, and a single multi-domain-optimized solution is not guaranteed. The search for a design which presents satisfactory merits becomes a drudge through various trade-off studies which can expend vast quantities of manpower and time. As a remedy to this, design automation allows the process to be computer-assisted. This dissertation presents the core fundamentals for a general multi-objective design optimization framework intended for the design …

Investigations Of Conjugate Heat Transfer And Fluid Flow In Partitioned Porous Cavity Using Darcy-Forchheimer Model: Finite Element-Based Computations, Nasir Yasin, Shafee Ahmad, Muhammad Umair, Zahir Shah, Narcisa Vrinceanu, Ghadah Alhawael

Mathematics & Statistics Faculty Publications

The conjugate heat transfer and fluid flow has vast applications in thermal engineering, particularly for cooling in thermal devices, and automobile engines. This study investigates conjugate heat transfer in 2D enclosures, featuring thin solid fins attached to a porous bottom wall. The porous medium is considered isotropic and homogeneous by the Darcy-Forchheimer model, with fluid phases in local thermal equilibrium. The boundary conditions at the porous fluid interface ensure continuity of the velocities, stresses, temperature, and heat flux. The phenomenon is mathematically modelled by obtaining a set of partial differential equations. The finite element method (FEM) is used to perform …

Enhance The Design Of Low-Cost Fast Charging Battery Systems For Electric Mobility Systems, Omar Matar, Abdalrahman S. Alneklawy, Yara M. El-Hawary, Ahmed M. Elbeshbeshy, Ali Shoman, Ahmed R. Alagmy, Ahmed Mashaly, Arwa G. Saheen, Sahar S. Kaddah, Basem M. Badr

Mansoura Engineering Journal

The need of electric mobility (E-Mobility) systems increases daily, where the E-Mobility systems contribute in decreasing gas emissions from transportation Electric motorcycles (E-Motorcycles) are one of the E-Mobility systems, which reduce the problems resulting from traditional fossil fuel exhausts. This paper discusses the design and development of low-cost battery systems for E-Motorcycles, where a fast charging system is simulated, analyzed, and deployed to charge a battery package that outputs 72V & 8A at rated performance. Research and analysis of different power converter topologies are performed with respect the cost and system performance. A battery tester circuit is designed and built …

Renewable Energy Integration In Nyc Subway: Solar Panels (Under Railroads And At The Subway's Edge) & Vertical Axis Wind Turbines (Vawts) Near Tracks, Pengdwinde Olivier Saba

Dissertations and Theses

As urban populations continue to expand, the demands on public transit systems in densely populated regions like New York City are escalating. Multiple cities face the dual challenge of increasing ridership and the imperative to minimize their environmental footprints.According to Law 97 in NYC, the goal is to reduce emissions by 40% and to reach net zero by 2050. An innovative solution will need to be integrated into the NYC environment, such as the subway system, where there are a potential unuse space, building rooftop, since we know that spaces in New York City are very critical, such as these …

A Comprehensive Academic And Industrial Survey Of Blockchain Technology For The Energy Sector Using Fuzzy Einstein Decision-Making, Umit Cali, Annabelle Lee, Barry Hayes, Claudio Lima, D. Jonathan Sebastian-Cardenas, David Flynn, Emre Kantar, Farrokh Rahimi, Kaung Si Thu, Marco Pasetti, Marthe Fogstad Dynge, Merlinda Andoni, Muhammet Deveci, Murat Kuzlu, Raquel Alanso, Kim-Kwang Raymond Choo, Sambeet Mishra, Shammya Shananda Saha, Sonam Norbu, Srinikhil Gourisetti, Ugur Halden, Vahid Hosseinezhad, Valentin Robu

Engineering Technology Faculty Publications

The global energy sector is undergoing a significant transformation driven by decarbonization and digitalization, leading to the emergence of Distributed Ledger Technology (DLT) — particularly blockchain — as a promising tool for enhancing transparency, security, and efficiency in modern power systems. This study aims to provide a comprehensive academic and industrial survey of blockchain applications in the energy sector and develop a robust decision-making framework to identify and prioritize the most promising real-world use cases based on multidisciplinary criteria. A three-stage methodology was adopted: (i) a literature and market review encompassing over 300 academic publications and commercial blockchain initiatives in …

Advancing 4h-Silicon Carbide Power Mosfets Through Three-Dimensional Technology Computer Aided Design Optimization, Skylar A. Deboer

Electronic Theses & Dissertations (2024 - present)

This research contributes to the advancement of 1.2 kV 4H-Silicon Carbide (SiC) Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) through the development and implementation of sophisticated three-dimensional Technology Computer-Aided Design (3D TCAD) methodologies. These advanced simulation techniques enable comprehensive evaluation of innovative unit cell architectures that remain inaccessible to conventional two-dimensional (2D) TCAD approaches. By leveraging these 3D simulation capabilities, this work facilitates significant improvements in both the performance and reliability of next-generation wide-bandgap power devices.

Power devices convert electrical energy between different forms throughout the power grid making them essential in applications ranging from personal electronics, electric vehicles, renewable energy systems, industrial …

Dc To Ac Voltage Source Converter Control System, Mohummad Tark Elgassier, Jordan M. Reichhardt, Brayden Richard Young

Electrical Engineering

The Voltage Source Converter Control System integrates a control system for a 2-phase half-bridge voltage source converter (VSC) utilizing a TMS320F2837xD Dual-Core Real-Time Microcontroller. VSCs are critical for the integration of power production into the power grid. They allow for accurate control over DC-AC energy conversion. Additionally with the improvement of renewable energies VSCs play a critical role in controlling the flow of these environmentally controlled and more unpredictable sources of energy. The control system regulates the output current and voltage, regulates switching patterns, and maintains power stability under varying loads. Within the control system Digital control systems, PWM generation, …

Laser-Induced Graphene For Early Disease Detection: A Review, Sri Ramulu Torati, Gymama Slaughter

Center for Bioelectronics Publications

Electrochemical biosensors have been instrumental in early disease detection, facilitating effective monitoring and treatment. The emergence of graphene has significantly advanced sensor technology in various fields, including biomedicine, electronics, and energy. In this landscape, laser‐induced graphene (LIG) has emerged as a superior alternative to conventional graphene synthesis methods. Its straightforward fabrication process and compatibility with wearable devices boost its practicality and potential for real‐world applications. This review highlights the transformative potential of LIG in biosensing, showcasing its contributions to the development of next‐generation diagnostic tools for early disease detection. An overview of the LIG synthesis process and its applications in …

Streamer Discharge Modeling For Plasma-Assisted Combustion, Stuart Reyes, Shirshak Kumar Dhali

Electrical & Computer Engineering Faculty Publications

Some of the popular and successful atmospheric pressure fuel/air plasma-assisted combustion methods use repetitive ns pulsed discharges and dielectric-barrier discharges. The transient phase in such discharges is dominated by transport under strong space charge from ionization fronts, which is best characterized by the streamer model. The role of the nonthermal plasma in such discharges is to produce radicals, which accelerates the chemical conversion reaction leading to temperature rise and ignition. Therefore, the characterization of the streamer and its energy partitioning is essential to develop a predictive model. We examine the important characteristics of streamers that influence combustion and develop some …

Distributed Energy Resources (Der) Capacity Estimation With Spatiotemporal Downscaling For Transmission And Distribution Coordination, Abhilasha Suvedi

Electronic Theses and Dissertations

The primary objective of this thesis is to accurately estimate the capacity of distributed energy resources (DERs) using the spatiotemporally downscaled local solar irradiance for their efficient integration into transmission-level grid operations. Accurate estimation of DER capacity is crucial for their enhanced integration into real-time electricity markets. Different methods of spatiotemporally downscaling the solar irradiance are presented in this thesis, followed by their integration into the 12 house distribution network – a low voltage residential distribution system. Grid support functions (GSFs) like Volt-Watt Control, Volt-VAR control, and Frequency-Watt Control are applied to effectively regulate the frequency and maintain the voltage …

Solar Energy Prediction Using Advanced Hybrid Machine Learning Models, Jelawi A. Alqhtani

UNF Graduate Theses and Dissertations

Accurate short-term forecasting of solar power generation is critical for the reliable and cost-effective operation of renewable-based microgrids, where sudden weather-induced variability can compromise grid stability, battery scheduling, and energy trading decisions. Traditional physical and statistical models struggle to capture the complex non-linear relationships and localized weather effects, while individual deep learning architectures often exhibit systematic biases such as chronic under-prediction of peak generation. This thesis proposes a novel Cross-Feedback Ensemble framework that combines the complementary strengths of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and one-dimensional Convolutional Neural Network (1D-CNN) models through an iterative cross-feedback mechanism and a …

Flame Boundary Effects Of Hydrogen-Air Premixing, Alecson L. De Lima Junior

Honors Undergraduate Theses

The objective of this research was to investigate the reaction of hydrogen and heated air premixing when injected into a combusting ethylene-air crossflow, and to study the flame stabilization location with varying premixing levels. The main parameter of this investigation was the flame stabilization diagnosed through chemiluminescence, based on recorded equivalence ratio, temperature, and flame boundaries, and maintaining a constant air temperature, hydrogen and air mass flow, and momentum flux ratios. Testing was conducted at different ratios of hydrogen and air premixing achieved through alternating the distance between the point where hydrogen and air are mixed and the combusting crossflow. …

High-Fidelity Soh Prediction In Lithium-Ion Batteries Using Hybrid Ml Networks, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

Accurate and efficient prediction of lithium-ion battery state of health (SOH) is critical for ensuring reliability in electric vehicles, grid storage, and aerospace systems. Traditional SOH estimation methods often struggle with nonlinear degradation behaviors and lack sensitivity to subtle electrochemical signals, limiting their real-world deployment. To address these challenges, this study examines hybrid deep learning models that integrate differential capacity (dQ/dV) analysis to enhance predictive accuracy. Four hybrid architectures - hybrid CNN-LSTM multihead, CNN extractor for LSTM, DNN-LSTM, and DNN Bi-LSTM - were developed and evaluated using the NASA randomized battery usage dataset, offering a realistic benchmark under diverse operational …

A Proximal Policy Optimization-Based Controller For Enhanced Power Sharing In Microgrids, Seyedmohammad Hasheminasab, Armin Lotfy, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

This paper introduces a Proximal Policy Optimization (PPO)-based virtual impedance (VI) controller to enhance both power sharing and system response under disturbances in inverter-interfaced microgrids. Traditional droop control methods often face challenges due to variations in feeder impedance, which degrade performance. The proposed controller continuously updates its policy based on changes in the operating environment. The control problem is modeled as a Markov Decision Process (MDP), in which the state and action spaces are explicitly defined, and a carefully designed reward function, satisfying system criteria and constraints, guides the learning process toward achieving the desired transient and steady-state performance. By …

Energy-Aware Swarm Robotics In Smart Microgrids Using Quantum-Inspired Reinforcement Learning, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based …

Energy-Aware Sensor Fusion Architecture For Autonomous Channel Robot Navigation In Constrained Environments, Mohamed Shili, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

Navigating autonomous robots in confined channels is inherently challenging due to limited space, dynamic obstacles, and energy constraints. Existing sensor fusion strategies often consume excessive power because all sensors remain active regardless of environmental conditions. This paper presents an energy-aware adaptive sensor fusion framework for channel robots that deploys RGB cameras, laser range finders, and IMU sensors according to environmental complexity. Sensor data are fused using an adaptive Extended Kalman Filter (EKF), which selectively integrates multi-sensor information to maintain high navigation accuracy while minimizing energy consumption. An energy management module dynamically adjusts sensor activation and computational load, enabling significant reductions …

Advances In Battery Modeling And Management Systems: A Comprehensive Review Of Techniques, Challenges, And Future Perspectives, Seyed Saeed Madani, Yasmin Shabeer, Ananthu Shibu Nair, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, Shi Xue Dou, Khay See, Saad Mekhilef, Françios Allard

Electrical & Computer Engineering Faculty Publications

Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include …

Energy-Based Deep Incomplete Multi-View Clustering, Ziyu Wang, Yiming Du, Rui Ning, Lusi Li

Computer Science Faculty Publications

Incomplete multi-view clustering (IMVC) deals with real-world scenarios where certain views are partially missing, posing significant challenges to effective clustering. Most existing IMVC approaches face a trade-off: imputation-free methods suffer from information bias and imbalance, while full-imputation methods risk introducing and propagating noise. To overcome these limitations, we propose Energy-Based Deep Incomplete Multi-View Clustering (Energy-DIMC), a novel selective-imputation framework that leverages energy-based models (EBMs) to guide reliable imputations and robust clustering. EBMs assess data compatibility by assigning lower energy to more coherent structures, effectively modeling complex inter-view and inter-sample dependencies. Inspired by EBMs, Energy-DIMC integrates four key components: 1) a …

Snake Optimization Algorithm For Fractional Order Controller In Three-Area Load Frequency Regulation, Shubra Goel, Omveer Singh

ASEAN Journal on Science and Technology for Development

The transition toward renewable energy-dominated power systems have accentuated the intricacies of frequency control, necessitating advanced regulatory mechanisms. This investigation articulates a tri-zonal frequency stabilization approach, employing a hybridized controller that synergizes Fuzzy Fractional-Order PI and Tilt-Integral-Derivative methodologies. The uniqueness of this approach is further amplified by the deployment of the Snake Optimization algorithm for precise parameter tuning. Set within a conventional grid topology integrated with assorted renewable energy sources, the study evaluates the controller’s adaptability and resilience through a series of comprehensive scenario-based analyses.