Power Quality Event Diagnosis Using Multi-Rate Neural Networks,
2025
University of Texas at Arlington
Power Quality Event Diagnosis Using Multi-Rate Neural Networks, Jordan D. Lloyd
Electrical Engineering Theses - Archive
Power quality disturbances (PQDs) are among the primary challenges facing modern electrical systems, as they degrade the performance and lifespan of connected equipment. This thesis investigates the relationship between the rate at which voltage waveform data are sampled, the reliability of these measurements, and the ability of deep neural networks to classify PQDs accurately. A one-dimensional convolutional neural network (CNN) was trained and evaluated across multiple sampling rates and signal-to-noise ratios to quantify how information loss in the temporal and spectral domains affects classification reliability. The results demonstrate that model accuracy degrades nonlinearly as sampling rate and signal-to-noise ratio (SNR) …
Characterization Of Dc Arc Flash Events Generated By Electrochemical Energy Storage,
2025
University of Texas at Arlington
Characterization Of Dc Arc Flash Events Generated By Electrochemical Energy Storage, Nicolaus E. Jennings
Electrical Engineering Dissertations - Archive
The increasing rise in the use of electrochemical energy storage (ECES) like in the form of valve regulated lead acid (VRLA) batteries, lithium-ion (Li-ion) batteries, electric double layer capacitors (EDLC), and metalized film, oil filled capacitors prompt new challenges concerning electric worker safety. The primary safety hazards associated with ECES are electric shock and arc flash. The electric shock hazard is well understood to the extent where it is known what potential and exposure duration will cause levels of pain and ultimately fatality. Various personal protective equipment (PPE) like insulating gloves allow electric workers to perform maintenance with sufficient protection …
Enhanced Load Detection With Data-Driven Appliance Signatures Using Mixed Integer Linear Programming In Non-Intrusive Load Monitoring,
2025
University of Texas at Arlington
Enhanced Load Detection With Data-Driven Appliance Signatures Using Mixed Integer Linear Programming In Non-Intrusive Load Monitoring, Marina Materikina
Industrial, Manufacturing, and Systems Engineering Dissertations - Archive
Despite the numerous research studies and interest in the non-intrusive load monitoring (NILM) area to improve energy efficiency, the problem of accurate and precise disaggregation of electrical devices has not been solved yet. The goal of our research is to build a method with a focus on higher accuracy on complex state-based appliances, which most approaches struggle to detect due to their power signal complexity and low consumption. Our approach is NILM with data-driven signatures (DS), with the ability to potentially predict power usage over time that would work great for suitable applications such as demand response, anomaly detection, and …
Analytical Model Of The Ac-Ac Dab Converter In The Egam Framework,
2025
Missouri University of Science and Technology
Analytical Model Of The Ac-Ac Dab Converter In The Egam Framework, Arnold A. Fernandes, Kartikeya Jayadurga Prasad Veeramraju, Jonathan W. Kimball
Electrical and Computer Engineering Faculty Research & Creative Works
This paper develops an analytical model of the bidirectional AC-AC Dual Active Bridge (DAB) converter. The passive components of the AC-AC DAB are subject to grid, switching, and sideband harmonics. Thus, it is impossible to model via the conventional Generalized Average Method (GAM). It has been numerically shown that Extended GAM (EGAM) can be used to model the AC-AC DAB converter. In this paper, an analytical sixteenth order EGAM-model has been developed that considers only grid harmonics at the filter components and only sideband harmonics for the transformer leakage inductor. A closed-form expression is developed for the 2D convolution product. …
Determining The Transient Electric Field And The Effect Of Pulse Repetition Frequency On The Field In A Repetitive 10-Ns Pulsed Discharge In A Quiescent Ch₄-Air Mixture,
2025
Old Dominion University
Determining The Transient Electric Field And The Effect Of Pulse Repetition Frequency On The Field In A Repetitive 10-Ns Pulsed Discharge In A Quiescent Ch₄-Air Mixture, Chunqi Jiang
Bioelectrics Publications
The goal of this research is to understand the underlying physics enabled by nanosecond pulsed power in a discharge initiation and the following physicochemical processes that favor lean-fuel plasma ignition for combustion. The hypothesis of this project is that pulsed power waveforms such as the pulse repetition frequency (PRF), voltage amplitude, and dielectric surface alter the reduced electric field during the initiation of the discharge, resulting in different plasma properties, which will vary the physicochemical processes for efficient and selective radical productions. This is especially important for lean-burn combustion and reducing emission.
Nanomaterial-Enabled Enhancements In Thylakoid-Based Biofuel Cells,
2025
Old Dominion University
Nanomaterial-Enabled Enhancements In Thylakoid-Based Biofuel Cells, Amit Sarode, Gymama Slaughter
Center for Bioelectronics Publications
Thylakoid-based photosynthetic biofuel cells (TBFCs) harness the inherent light-driven electron transfer pathways of photosynthesis to enable sustainable solar-to-electrical energy conversion. While TBFCs offer a unique route toward biohybrid energy systems, their practical deployment is hindered by sluggish electron transfer kinetics, unstable redox mediators, and inefficient interfacing between biological and electrode components. This review critically examines recent advances in TBFCs, with a focus on three key surface engineering strategies: (i) incorporation of nanostructured materials to enhance electrode conductivity and surface area; (ii) application of redox mediators to facilitate charge transfer between photosynthetic proteins and electrodes; and (iii) functional exploitation of individual …
Optimizing Material Selection And Operational Conditions For Xhv Systems: Lessons From Aisi 1020 And 316l Comparative Studies,
2025
Old Dominion University
Optimizing Material Selection And Operational Conditions For Xhv Systems: Lessons From Aisi 1020 And 316l Comparative Studies, Aiman H. Al-Allaq, Md Abdullah Mamun, Matt Poelker, Abdelmageed Elmustafa
Mechanical & Aerospace Engineering Faculty Publications
In this study, AISI 1020 low-carbon steel was investigated as a cost-effective alternative to SS316L stainless steel for reaching extreme high vacuum (XHV) conditions. After being baked at 400°C, a vacuum chamber made of low-carbon steel material exhibited an outgassing rate approximately 2000 times smaller than a similar chamber made of stainless steel. Its activation energy for hydrogen diffusion (27 kJ/mol) is less than half that of stainless steel (60.3 kJ/mol), indicating more efficient hydrogen removal during bakeout. MolFlow+ simulations supported the experimental data and demonstrated the importance of system geometry optimization and minimizing stainless steel content for achieving optimal …
A Comprehensive Review Of Piezoelectric Pvdf Polymer Fabrications And Characteristics,
2025
Old Dominion University
A Comprehensive Review Of Piezoelectric Pvdf Polymer Fabrications And Characteristics, Nadia Ahbab, Sidra Naz, Tian-Bing Xu, Shihai Zhang
Mechanical & Aerospace Engineering Faculty Publications
Polyvinylidene fluoride (PVDF) polymer films, renowned for their exceptional piezoelectric, pyroelectric, and ferroelectric properties, offer a versatile platform for the development of cutting-edge micro-scale functional devices, enabling innovative applications ranging from energy harvesting and sensing to medical diagnostics and actuation. This paper presents an in-depth review of the material properties, fabrication methodologies, and characterization of PVDF films. Initially, a comprehensive description of the physical, mechanical, chemical, thermal, electrical, and electromechanical properties is provided. The unique combination of piezoelectric, pyroelectric, and ferroelectric properties, coupled with its excellent chemical resistance and mechanical strength, makes PVDF a highly valuable material for a wide …
An Extendable Soft-Switched Step-Up Interleaved Converter Integrated With Voltage Multiplier Cells,
2025
Missouri University of Science and Technology
An Extendable Soft-Switched Step-Up Interleaved Converter Integrated With Voltage Multiplier Cells, Amir Hasan Babanezhad, Koosha Choobdari Omran, Reza Beiranvand, Pourya Shamsi
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a multiphase high step-up interleaved converter is introduced, which ensures low-input current ripple, low-component currents, and voltage stresses. The proposed circuit guarantees ZVS operation for power switches and ZCS operation for power diodes, which significantly reduce converter switching losses and EMI emission and improve its efficiency. Therefore, its passive components volume can be reduced by using high switching frequencies. In addition, the interleaved technique reduces input current ripple and input filter volume and provides high-power density. High-voltage gain and low-voltage stresses on the components are also achieved due to the integration of the converter structure with the …
Structural Analysis Of Multi-Domain Dynamic Systems Modeled Via Bond Graphs,
2025
Missouri University of Science and Technology
Structural Analysis Of Multi-Domain Dynamic Systems Modeled Via Bond Graphs, Arnold A. Fernandes, Jonathan W. Kimball
Electrical and Computer Engineering Faculty Research & Creative Works
Structural Observability (SO) and Structural Monitorability (SM) are structural properties utilized to determine the state and fault-free operation of components, respectively, in a bond graph (BG) model. BGs enable qualitative system analysis, evaluating whether existing sets of sensors and actuators ensure Structural Observability (SO) and Structural Controllability (SC) without knowledge of parametric values. Furthermore, the analysis determines whether there are sufficient sensors available to identify component faults accurately. This work provides a framework for automated sensor placement in a multi-domain physical system while analyzing the SO and SM properties. The MATLAB Structural Analysis Toolbox (MATSAT) conducts sensor placement in a …
A High Step-Up Soft-Switched Converter Based On Coupled Inductor And Current-Fed Voltage Multiplier,
2025
Missouri University of Science and Technology
A High Step-Up Soft-Switched Converter Based On Coupled Inductor And Current-Fed Voltage Multiplier, Koosha Choobdari Omran, Reza Beiranvand, Pourya Shamsi
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a novel non-isolated DC-DC converter that combines coupled inductor (CI) and voltage multiplier (VM) techniques is proposed. The leakage energy of the CI is effectively recycled, and soft-switching conditions are achieved for all switches and diodes. Resonance between the leakage inductor of the CI and VM capacitors provides soft-switching conditions, without requiring a separate resonant tank. The use of VM stages not only lowers the voltage stress on semiconductor components but also allows for the use of low-voltage-rated devices, leading to reduced conduction losses, lower cost, and improved efficiency. High voltage gain can be achieved by appropriately …
Modeling And Analysis Of Amorphous Steel Transformer For Potential Loss Reduction In Power Systems.,
2025
University of Kentucky
Modeling And Analysis Of Amorphous Steel Transformer For Potential Loss Reduction In Power Systems., Daniel A. Muchow
Theses and Dissertations--Electrical and Computer Engineering
The energy consumed in power systems can be reduced directly by implementing new technologies and materials. The effort to reduce the carbon emissions expelled from the production of electrical energy has become a major focus. According to the U.S. energy Information Administration approximately 5% of all the electric generated and transmitted in the U.S. electrical grid is lost. The energy profile calculated from the usage of electrical power from all 50 states in 2021 is estimated to be 3.8 billion megawatts per hour, with a state average of just over 11 cents per MWh, producing just over 41.8 billion in …
Age Of Information-Based Optimal Scheduling With Energy Cost Trade-Off For Smart Warehouse: A Deep Reinforcement Learning-Based Approach,
2025
Old Dominion University
Age Of Information-Based Optimal Scheduling With Energy Cost Trade-Off For Smart Warehouse: A Deep Reinforcement Learning-Based Approach, Sandip Roy, Abhishek Bisht, Ashok Kumar Das, Sachin Shetty
VMASC Publications
Recent advances in the integration of high-speed mobile networks and real-time IoT devices have facilitated in building of smart warehouses, where a set of beacons and Internet of Things (IoT) devices (or source nodes) can monitor the status of various physical processes in a time-critical way. In real-time status monitoring systems, like smart warehouses, quantifying the freshness of the Internet of Things (IoT) data based on the age of information (AoI) metrics becomes quite crucial. As source nodes are battery-constrained, a balanced trade-off between AoI minimization and preservation of source node battery energy is essential. In this paper, in a …
A Comparative Analysis Of Preprocessing Filters For Deep Learning-Based Equipment Power Efficiency Classification And Prediction Models,
2025
Ulsan National Institute of Science and Technology
A Comparative Analysis Of Preprocessing Filters For Deep Learning-Based Equipment Power Efficiency Classification And Prediction Models, Sang-Ha Sung, Chang-Sung Seo, Michael Pokojovy, Sangjin Kim
Mathematics & Statistics Faculty Publications
The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a Transformer model that predicts power efficiency states (Normal, Caution, Warning) from minute-level IIoT sensor data. We evaluated five techniques: a baseline, Simple Moving Average, Median filter, Hampel filter, and Kalman filter. For each technique, we conducted systematic experiments across time windows (360 and 720 min) that reflect real-world industrial inspection cycles, along with five prediction offsets (up …
Assessing Tidal Energy Potential In The Visayas: Viability Of The San Bernardino, San Juanico, And Cebu Straits,
2025
Ateneo de Manila University
Assessing Tidal Energy Potential In The Visayas: Viability Of The San Bernardino, San Juanico, And Cebu Straits, Justin Kyle O. Ricafort, King Harold A. Recto
Sustainability, Disaster Science and Energy
By laying the groundwork for sustainable tidal energy infrastructure, this study contributes to advancing the Philippines' renewable energy portfolio and supports the global transition to clean energy solutions. With the country's extensive coastline and rising energy demands, tidal energy presents a largely underutilized yet promising resource that can address both local and global energy challenges. Tidal energy is highly predictable, stable, and environmentally friendly, offering a reliable alternative to conventional energy sources like coal and natural gas, which are often subject to price volatility and environmental concerns. The study focuses on the Visayas region, a prime candidate for tidal energy …
High-Fidelity Soh Prediction In Lithium-Ion Batteries Using Hybrid Ml Networks,
2025
Old Dominion University
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,
2025
Carleton University
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,
2025
Innov'COM Laboratory
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 …
Streamer Discharge Modeling For Plasma-Assisted Combustion,
2025
Old Dominion University
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 …
Advances In Battery Modeling And Management Systems: A Comprehensive Review Of Techniques, Challenges, And Future Perspectives,
2025
University of Waterloo
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 …
