Open Access. Powered by Scholars. Published by Universities.®

Electrical and Computer Engineering Commons™

Open Access. Powered by Scholars. Published by Universities.®

University of Kentucky

Discipline
Keyword
Publication Year
Publication
Publication Type

Articles 1 - 30 of 680

Full-Text Articles in Electrical and Computer Engineering

Utility-Scale Battery Energy Storage Systems - Field Pilot Unit Experience, Analysis, And Review Of Technological Advancements, Kwabena A. Kyeremeh, Shaun E. Lavin, Grant M. Fischer, Dan M. Ionel, Aron Patrick May 2026

Utility-Scale Battery Energy Storage Systems - Field Pilot Unit Experience, Analysis, And Review Of Technological Advancements, Kwabena A. Kyeremeh, Shaun E. Lavin, Grant M. Fischer, Dan M. Ionel, Aron Patrick

Electrical and Computer Engineering Graduate Research

Utility-scale Battery Energy Storage Systems (BESS) are becoming increasingly important in modern power systems, providing services ranging from backup support to load balancing and peak shaving. The long-term operational performance and reliability of these systems with expanding deployment remain insufficiently documented. This paper provides an operational assessment of a 1MW/2MWh utility-scale BESS alongside a systematic review of technology developments across major subsystems. Operational metrics including round-trip efficiency (RTE), outage characteristics, and degradation patterns are examined to identify performance trends and the underlying causes of outages. In addition, testing and operational principles aligned with utility practice and standards are presented to …

Go to article

Study Of Clustering Technique And Communication Topologies For Cooperative Control-Based Volt-Var Optimization, Gaurav Yadav, Yuan Liao, Dan M. Ionel Jan 2026

Study Of Clustering Technique And Communication Topologies For Cooperative Control-Based Volt-Var Optimization, Gaurav Yadav, Yuan Liao, Dan M. Ionel

Electrical and Computer Engineering Faculty Publications

Introducing renewable distributed generation (DG) in the power distribution system causes rapid voltage fluctuations due to its intermittency. This intermittency renders conventional voltage regulation devices such as on-load tap changers (OLTCs) and capacitor banks (CBs) inefficient to regulate rapid voltage changes and leads to reduced equipment lifetime and high operation and maintenance costs. Hence, this calls for non-conventional methods to mitigate such voltage fluctuations. This paper presents a cooperative control-based method aimed to optimally control the reactive power of DG inverters to mitigate the voltage deviations by establishing communication among the DG nodes, and between DG and non-DG nodes. This …

Go to article

Heat Input Control And Deep Learning-Based Indirect Measure Of Process And Deposition Stability In Wire Arc Additive Manufacturing, Alessandra Caggiano, Giulio Mattera, Yuming Zhang, Roberto Teti Jan 2026

Heat Input Control And Deep Learning-Based Indirect Measure Of Process And Deposition Stability In Wire Arc Additive Manufacturing, Alessandra Caggiano, Giulio Mattera, Yuming Zhang, Roberto Teti

Electrical and Computer Engineering Faculty Publications

A process qualification-oriented data-driven framework for Wire Arc Additive Manufacturing (WAAM) integrating qualification data, process monitoring and feedback control, is presented. A proportional control strategy regulating heat input by varying the Contact Tip–to–Workpiece Distance (CTWD) is developed to enhance process stability, ensure consistent layer geometry and maintain the qualified heat-input conditions for process qualification. To assess the control strategy stability, deep learning-based CTWD soft sensing from high-frequency welding signals is combined with an uncertainty-aware process quality index. The framework is validated on Invar 36 alloy, but it supports extension to other alloys and arc welding-based additive processes.

Go to article

Goal-Driven Shared Control In Eeg-Based Brain Machine Interface For Freewill Reaching And Grasping With Movement Intention Detection And Goal Position Decoding, Bhoj Raj Thapa Jan 2026

Goal-Driven Shared Control In Eeg-Based Brain Machine Interface For Freewill Reaching And Grasping With Movement Intention Detection And Goal Position Decoding, Bhoj Raj Thapa

Theses and Dissertations--Electrical and Computer Engineering

Upper limb motor impairments can severely limit a person’s ability to perform everyday reaching and grasping tasks. Electroencephalogram (EEG)-based brain machine interfaces (BMIs) offer a non-invasive approach for translating neural activity into control signals for assistive devices such as robotic arms. However, traditional EEG-based BMI studies have generally focused on externally cued paradigms, where both movement timing and target selection are specified by the experimenter rather than freely chosen by the user. In addition, shared control offers a practical framework for assistive BMI operation by dividing responsibility between the user and the intelligent robotic system. However, in many EEG-based shared …

Go to article

Structured Laser Vision-Based Measurement Of Gta-Weld Pool, Gang Zhang, Jianbo Wang, Yu Shi, Ding Fan, Yuming Zhang Jan 2026

Structured Laser Vision-Based Measurement Of Gta-Weld Pool, Gang Zhang, Jianbo Wang, Yu Shi, Ding Fan, Yuming Zhang

Electrical and Computer Engineering Faculty Publications

The current study of weld pool fluid dynamics in arc welding focuses on the numerical model establishment and simulation, and the x-ray combined with particle trace imaging observations, there is no real-time monitor and quantitatively characterize the weld pool flow behavior in welding process for controlling the weld quality. This study develops an innovative structured laser vision-based sensing system for three-dimensional (3D) reconstruction and quantitative analysis of weld pool surface topographies in gas tungsten arc welding (GTAW). Through characterization of dynamic weld pool morphologies, two novel parameters are proposed: the surface convexity variation rate (Rh) and fluid …

Go to article

Ev Charging And V2g Operation For Distribution System Vpp Including Model Predictive Control, Rosemary E. Alden, Simone Silvestri, Malcolm D. Mcculloch, Dan M. Ionel Jan 2026

Ev Charging And V2g Operation For Distribution System Vpp Including Model Predictive Control, Rosemary E. Alden, Simone Silvestri, Malcolm D. Mcculloch, Dan M. Ionel

Electrical and Computer Engineering Faculty Publications

Future smart grid virtual power plants (VPPs) are considered for development based on industry communication standards for electric vehicle (EV) chargers such as Open Charge Point Protocol (OCPP), IEC 15118, and IEC 61851. To support research and development of computationally intelligent controls for distributed EV batteries, a python-based API OpenDSS VPP framework is utilized with thousands of experimental smart meter profiles, the IEEE 123 node test feeder, and hundreds of national survey-based EV modules for conventional and optimal charging and vehicle-to-grid (V2G) control development to mitigate any voltage violations and reduce peak load. A methodology is proposed for model-predictive control …

Go to article

Multi-Objective Optimization Of Energy Costs And Ev Battery Health In V2g Enabled Homes, Dzifa M. Hodey Jan 2026

Multi-Objective Optimization Of Energy Costs And Ev Battery Health In V2g Enabled Homes, Dzifa M. Hodey

Theses and Dissertations--Computer Science

Electric vehicles (EVs) and rooftop solar photovoltaic (PV) systems are increasingly being integrated into residential settings, creating new opportunities for vehicle-to-grid (V2G) and vehicle-to-home (V2H) operations. In these systems, the EV battery functions as a controllable energy storage unit that can charge from the grid or PV and discharge energy to supply household load or export to the grid for a profit. By intelligently scheduling this bidirectional power exchange, households can reduce electricity costs and enhance PV utilization. Realizing these benefits requires optimization strategies that balance cost reduction with EV battery health preservation. However, existing V2G/V2H studies largely emphasize cost …

Go to article

Advancing Task-Oriented Dialog Systems: Scalability, Generalization, And Evaluation, Adib Mosharrof Jan 2026

Advancing Task-Oriented Dialog Systems: Scalability, Generalization, And Evaluation, Adib Mosharrof

Theses and Dissertations--Computer Science

Task-oriented dialog (TOD) systems enable conversational interfaces for complex tasks like flight booking and restaurant reservations. However, deploying TOD systems at scale faces three critical barriers: scalability, generalization, and evaluation. Scalability is primarily restricted by the human-annotation bottleneck, as current systems depend on vast quantities of manually labeled data for every new domain, making deployment prohibitively expensive. Generalization remains a persistent challenge, as systems optimized for known domains often suffer significant performance degradation when encountering new, unseen ones. Existing evaluation metrics measure response quality and fluency, but fail to measure functional task success. As TOD systems are deployed across diverse …

Go to article

Vision‑Based Online Quality Tracking In Wire Arc Additive Manufacturing Via Hybrid Unsupervised Deep Learning–Statistical Process Monitoring, Giulio Mattera, Yue Cao, Yuming Zhang, Luigi Nele Jan 2026

Vision‑Based Online Quality Tracking In Wire Arc Additive Manufacturing Via Hybrid Unsupervised Deep Learning–Statistical Process Monitoring, Giulio Mattera, Yue Cao, Yuming Zhang, Luigi Nele

Electrical and Computer Engineering Faculty Publications

Vision-based monitoring of Wire Arc Additive Manufacturing (WAAM) using supervised deep learning represents the state of the art in anomaly detection, but such approaches require large labeled datasets that are costly to obtain and typically limited to laboratory conditions. To address these limitations, this work proposes a hybrid deep learning–statistical process monitoring (SPM) framework tailored to the stochastic nature of conventional arc welding processes such as GMAW-based additive manufacturing, where existing methods often overfit. The framework integrates a residual convolutional autoencoder (Res-CAE) with skip connections, which jointly analyzes video frames to generate refined latent-space features that are subsequently monitored using …

Go to article

Numerical Analysis And Simulation Of Enhanced Performance In Nanowire Cds/Cdte Solar Cells: A Pathway To Greater Than 25% Efficient Cdte Solar Cell, Riasad Badhan Jan 2026

Numerical Analysis And Simulation Of Enhanced Performance In Nanowire Cds/Cdte Solar Cells: A Pathway To Greater Than 25% Efficient Cdte Solar Cell, Riasad Badhan

Theses and Dissertations--Electrical and Computer Engineering

This Thesis finds a pathway to a significantly high-efficient CdTe based solar cell by demonstrating and harvesting the advantages of a nano-structure configuration in CdTe based solar cells. Nanowire CdS window layer and the “control”, planar CdS window layer films were fabricated in the laboratory and compared for their optical transmission and other characteristics affecting the performance of the CdS-CdTe solar cell. Numerical simulations were performed for a comparative evaluation of the embedded nanowire CdS-CdTe solar cell device and the traditional planar CdS-CdTe solar cell device. Experimentally measured spectral transmission of nanowire CdS film was used in the simulation environment. …

Go to article

Robotizing Complex Welding Processes Through Imitation Learning And Generative Models From Human Demonstration, Yue Cao Jan 2026

Robotizing Complex Welding Processes Through Imitation Learning And Generative Models From Human Demonstration, Yue Cao

Theses and Dissertations--Electrical and Computer Engineering

Arc welding processes demand real-time adaptive control that current robotic systems cannot achieve autonomously. This dissertation develops a systematic framework to robotize complex welding by learning from human demonstration, integrating generative modeling, physics-informed reconstruction, and model-based imitation learning. First, human--robot collaboration systems are established for both Gas Tungsten Arc Welding (GTAW) and Double-Electrode Gas Metal Arc Welding, combining robotic teleoperation with virtual reality interfaces to capture high-quality operator demonstrations. Second, a physics-informed neural network framework reconstructs complete molten pool flow fields from high-speed imaging, enriching process understanding beyond direct sensor observation. Third, generative models, including a hybrid latent variational autoencoder …

Go to article

Design Of Energy-Efficient, Scalable, And Flexible Tensor Processing Architectures With Electro-Photonic Integrated Circuits, Oluwaseun Alo Jan 2026

Design Of Energy-Efficient, Scalable, And Flexible Tensor Processing Architectures With Electro-Photonic Integrated Circuits, Oluwaseun Alo

Theses and Dissertations--Electrical and Computer Engineering

In recent years, artificial intelligence has achieved remarkable success across domains such as computer vision, natural language processing, and scientific computing. This progress has been driven largely by advances in deep learning, particularly deep neural networks (DNNs), including convolutional neural networks (CNNs) and transformer-based models. While these models deliver unprecedented accuracy, often surpassing human performance, their computational complexity continues to grow rapidly due to multibillion- and trillion-parameter designs. As model sizes and deployment scales expand, the demand for energy-efficient and high-throughput hardware accelerators has intensified. Conventional electronic platforms based on CPUs, GPUs, ASICs, and FPGAs are increasingly constrained by the …

Go to article

Frequency And Phase Synchronization Of Antenna Arrays With Phase Incoherent Microcontrollers, Harley W. Byrd Jan 2026

Frequency And Phase Synchronization Of Antenna Arrays With Phase Incoherent Microcontrollers, Harley W. Byrd

University of Kentucky Master's Theses

The construction of phased array antennas has traditionally been an expensive and complex task. It has recently been claimed that it is possible to construct high-performance Wi Fi antenna arrays using inexpensive consumer components. Motivated by some limited data available from online presentations of such devices, this thesis covers an attempt to construct a phased-array Wi-Fi antenna using several ESP32 chips, which have native Wi-Fi modulation and demodulation capabilities. While there are many positive aspects associated with utilizing ESP32 chips for this purpose, a key challenge is the inherent phase incoherence of their internal Phase Locked Loops (PLLs). The approach …

Go to article

High Torque Density Dual-Stator Vernier Motors With Flux Concentrating Rotors, Esmaeil Mohammadi, Ali Mohammadi, Mohammad Amin Jalali Kondelaji, Pedram Asef, Ion G. Boldea, Dan M. Ionel Oct 2025

High Torque Density Dual-Stator Vernier Motors With Flux Concentrating Rotors, Esmaeil Mohammadi, Ali Mohammadi, Mohammad Amin Jalali Kondelaji, Pedram Asef, Ion G. Boldea, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Axial Flux Permanent Magnet (AFPM) machines are increasingly being studied for low-speed, direct-drive applications due to their compact structure and high torque output. This study proposes two novel dual-stator AFPM vernier machine topologies: a spoke-type rotor configuration and a back-to-back Halbach array rotor. Both designs employ dual outer stators with 12 double-layer concentrated windings, and high-polarity rotor configurations to enhance flux concentration. A three-dimensional finite element model, which was previously validated by a laboratory prototype motor was utilized to evaluate the electromagnetic characteristics of the proposed topologies. These characteristics include torque density, airgap flux distribution, and harmonic content. Comparative results …

Go to article

Multi-Phase Wireless Power Transfer With High Power Density Inductive Coils For Electric Drone Charging, Lucas A. Gastineau, Donovin D. Lewis, Omer Onar, Dan M. Ionel Oct 2025

Multi-Phase Wireless Power Transfer With High Power Density Inductive Coils For Electric Drone Charging, Lucas A. Gastineau, Donovin D. Lewis, Omer Onar, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Wireless charging of unmanned ground vehicles and aircraft has been proposed to increase charging reliability and security, allow for autonomous functionality, and either reduce battery size or increase continuous flight time. This paper proposes a three-phase Litz wire primary and a two-phase PCB secondary for high secondary-side power density considering misalignment tolerances, surface and volumetric power density, and coil sizing. Electromagnetic 3D finite element analysis (FEA) simulations are conducted to study variation in mutual inductance and coupling coefficient with different secondary coil sizes and number of turns, horizontal and vertical misalignment between the primary and secondary, and a combination of …

Go to article

Nonlinear Design Scaling Of Electric Machines Based On Hybrid De And Meta-Modeling Application To Synchronous Motors With Combined Pm Stator And Reluctance Rotor Excitation, Oluwaseun A. Badewa, Dan M. Ionel Oct 2025

Nonlinear Design Scaling Of Electric Machines Based On Hybrid De And Meta-Modeling Application To Synchronous Motors With Combined Pm Stator And Reluctance Rotor Excitation, Oluwaseun A. Badewa, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents an innovative method for nonlinear scaling of electric machines by integrating machine learning (ML)-based meta-modeling with a differential evolution (DE) algorithm. The technique is applied to high-performance combined-excitation synchronous electric motors which exhibit highly nonlinear characteristics, making performance scaling challenging. The proposed approach employs an ML meta-model trained on data obtained from finite element analysis (FEA), utilizing an experimentally validated model for nonlinear scaling and performance prediction at different power ratings. The accuracy of the meta-model in capturing the nonlinear relationships between design parameters and motor performance is first assessed using metrics such as R-squared (R2) and …

Go to article

Multi Electric Machines With Series And Parallel Electromechanical Combinations For Aircraft, David R. Stewart, Donovin D. Lewis, Matin Vatani, Diego A. Lopez-Guerrero, Dan M. Ionel Oct 2025

Multi Electric Machines With Series And Parallel Electromechanical Combinations For Aircraft, David R. Stewart, Donovin D. Lewis, Matin Vatani, Diego A. Lopez-Guerrero, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

High-performance electric propulsion systems require fault tolerant, power dense, electric machines capable of maintaining high efficiency across a dynamic range of operation. To address these inherently conflicting requirements, multi-motor architectures employing electromechanically coupled modular configurations have been proposed to enhance system efficiency, fault tolerance, and redundancy. This paper investigates four mechanically coupled configurations for a coreless axial flux permanent magnet (CAFPM) motor unit integrating series, parallel, and hybrid architectures with differential and gearbox coupling. Performance and optimal sizing for motors in each configuration are determined through 3D finite element analysis (FEA). To assess fault tolerance and system redundancy, Markov chain …

Go to article

Design Optimization And Scaling Of Coreless Afpm Machines Using Hybrid Fea-Based Differential Evolution And Machine Learning, Matin Vatani, David R. Stewart, Donovin D. Lewis, Dan M. Ionel Oct 2025

Design Optimization And Scaling Of Coreless Afpm Machines Using Hybrid Fea-Based Differential Evolution And Machine Learning, Matin Vatani, David R. Stewart, Donovin D. Lewis, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents a machine learning (ML) based design framework for the fast and accurate optimization of coreless axial flux permanent magnet (AFPM) machines. Although the absence of magnetic cores eliminates material nonlinearity, the design process remains highly nonlinear due to the complex influence of geometric parameters. To overcome the computational challenges of finite element analysis (FEA)-based optimization, a series of multi-objective differential evolution (MODE) optimizations were conducted across various machine sizes at constant power output. The resulting design data was used to train an artificial neural network (ANN), enabling rapid prediction of machine performance without the need for repeated …

Go to article

Coreless Axial Flux Permanent Magnet Machines With Concentrated Coils And Various Pole/Coil Combinations, Matin Vatani, Spencer M. Goode-Kulchar, John F. Eastham, Xiaoze Pei, Dan M. Ionel Oct 2025

Coreless Axial Flux Permanent Magnet Machines With Concentrated Coils And Various Pole/Coil Combinations, Matin Vatani, Spencer M. Goode-Kulchar, John F. Eastham, Xiaoze Pei, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper comprehensively analyzes coreless stator axial flux permanent magnet (AFPM) machines by investigating rotor magnetic fields, stator winding factors, and 2D/3D finite element analysis (FEA) simulations. The torque production theory in coreless AFPM machines is studied with detailed derivations for flux density and current density. The impact of rotor permanent magnet (PM) width is examined for both surface-mounted and Halbach array configurations, followed by a discussion of its influence on the air-gap harmonic spectrum. The effect of stator coil side width is analyzed through a detailed winding factor study across various pole-to-coil ratios and a discussion on the trade-off …

Go to article

Voltage And Reactive Power Combined Control Of Utility Devices And Smart Inverters On A Distribution Grid With Solar Pv, Steven B. Poore, Rosemary E. Alden, Evan S. Jones, Thomas Morstyn, Aron Patrick, Dan M. Ionel Oct 2025

Voltage And Reactive Power Combined Control Of Utility Devices And Smart Inverters On A Distribution Grid With Solar Pv, Steven B. Poore, Rosemary E. Alden, Evan S. Jones, Thomas Morstyn, Aron Patrick, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

With adoption of distributed energy resources (DERs) expected in future grids, voltage regulation methods need to be reevaluated and improved to ensure their effectiveness under the high volatility of renewable generation. A multi-timescale cluster-based method is proposed to optimize and disperse operation of voltage controlling utility devices including capacitor banks (CBs) and load tap changers (LTCs) while allowing faster response time with customer-owned smart inverters (SIs) in-between switching operations. The proposed method is tested on a digital twin (DT) of a very large utility distribution grid with 2,018 nodes and 8.65MW peak load to evaluate its effectiveness in future grid …

Go to article

Torque-Speed Characteristic Estimation Based On Gaussian Processes And Adaptive Sampling Strategy For Permanent Magnet Synchronous Machines, Marcelo D. Silva, Pedram Asef, Oluwaseun A. Badewa, Rosemary E. Alden, Dan M. Ionel Oct 2025

Torque-Speed Characteristic Estimation Based On Gaussian Processes And Adaptive Sampling Strategy For Permanent Magnet Synchronous Machines, Marcelo D. Silva, Pedram Asef, Oluwaseun A. Badewa, Rosemary E. Alden, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Internal Permanent Magnet Synchronous Machines (IPMs) are widely used and typically optimized to meet specific performance requirements. Parameters such as base speed, maximum torque, and maximum speed commonly define the torque- speed characteristic of a given design. This study introduces a novel machine learning approach for statistically estimating the torque-speed characteristics of IPMs using Gaussian Process Regression (GPR), which models predictions as random variables. By leveraging uncertainty quantification, the study explores sampling strategies that enable the construction of a high-precision meta-model with minimal error and uncertainty. The proposed adaptive sampling strategy, combined with GPR, accurately estimates torque-speed characteristics and associated …

Go to article

Degradation Minimization Of Utility-Scale Li-Ion Bess Through Operational Optimization Employing An Equivalent Circuit Model, Kwabena A. Kyeremeh, Grant M. Fischer, Donovin D. Lewis, Aron Patrick, Dan M. Ionel Oct 2025

Degradation Minimization Of Utility-Scale Li-Ion Bess Through Operational Optimization Employing An Equivalent Circuit Model, Kwabena A. Kyeremeh, Grant M. Fischer, Donovin D. Lewis, Aron Patrick, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

The increasing deployment of utility-scale battery energy storage systems (BESS) necessitates effective strategies for supporting grid services while minimizing degradation that may compromise system longevity. High charge/discharge rates (C-rate) and imbalanced operation of multi-unit BESS configurations may accelerate degradation. This paper proposes a degradation-aware operational optimization based on Model Predictive Control (MPC) for coordinating multiple BESS units under physical and operational constraints. The multi-objective optimization model imposes penalties on C-rate magnitude, operational state-of-charge (SoC) disparity, and battery internal resistance modeled using an equivalent circuit model. A case study conducted for a fleet of BESS units over a one-week load profile …

Go to article

Data Center Developments For Flexible Generation Dispatch, Advanced Infrastructure, And Ultra-Fast Digital Twins, Grant M. Fischer, Rosemary E. Alden, Donovin D. Lewis, Aron Patrick, Dan M. Ionel Oct 2025

Data Center Developments For Flexible Generation Dispatch, Advanced Infrastructure, And Ultra-Fast Digital Twins, Grant M. Fischer, Rosemary E. Alden, Donovin D. Lewis, Aron Patrick, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

The rapid advancement and widespread integration of artificial intelligence (AI) is driving demand for unprecedented deployment of power-intensive computational infrastructure, including multi-megawatt data centers with the potential for facilities with gigawatt-scale capacity in the near future. In this paper, load growth projections for the US are reviewed, and an example energy dispatch solution considering a mixed energy portfolio with flexible, renewable, distributed, and load-based generation is employed. The brief technology review included in the paper covers aspects of electric power, cooling, and computational infrastructures. The concept of a data center digital twin for transient load, grid interaction, and hybrid energy …

Go to article

Report On The First Year Of Operation Of Kentucky’S First Utility Wind Turbine, Lawrence E. Holloway, Sophia A. Hahn, Aron Patrick Aug 2025

Report On The First Year Of Operation Of Kentucky’S First Utility Wind Turbine, Lawrence E. Holloway, Sophia A. Hahn, Aron Patrick

Electrical and Computer Engineering Faculty Publications

This report examines the first year’s operational data from Kentucky’s first utility wind turbine, the 37 m hub-height 90-kilowatt NPS100C-27, operated by the PPL Corporation Research and Development at their Renewable Integration Research Facility in Mercer County, Kentucky. During that year, the turbine was available 95% of the time, spinning 85% of the time, and generating power 78% of the time, and had a net capacity factor of 11%. This report analyzes the turbine performance and uses the collected wind data to project the performance of an example turbine more typical of larger commercial turbines recently installed elsewhere in the …

Go to article

Measurement Of Moisture Levels In Oils And Lubricants Using A Novel Moisture Sensor, Aaron Swartz, Ronak Ali Jun 2025

Measurement Of Moisture Levels In Oils And Lubricants Using A Novel Moisture Sensor, Aaron Swartz, Ronak Ali

Electrical and Computer Engineering Graduate Research

It is very challenging to measure moisture levels in oils. There is not a good method to measure it. Using the novel moisture sensor invented by Dr. Zhi David Chen, we tried various methods to measure the moisture levels in oils. The initial trials are to immerse the sensor chip into the oils to see any sensor reading changes with the change of moisture levels in oils. It was observed that the sensor reading was unstable even after immersion into the oil for two weeks. The idea for immersion of the sensor chip into the oils failed due to the …

Go to article

Aggregator Zone Selection For Ev Smart Controls Based-On Ml Clustering Of Grid Strength, Distance, And Charging Homogeneity, Rosemary E. Alden, Sam H. Lowe Ii, Dan M. Ionel Jun 2025

Aggregator Zone Selection For Ev Smart Controls Based-On Ml Clustering Of Grid Strength, Distance, And Charging Homogeneity, Rosemary E. Alden, Sam H. Lowe Ii, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Smart electric vehicle (EV) charging control methods from a central utility hub often require communication infrastructure over a large service area of electric power distribution systems with a large number of nodes. Industry standards such as Open Charge Point Protocol (OCPP) 2.1 have evolved to include topologies for local controllers to the individual chargers, i.e. EV aggregator zones. A machine learning (ML) application of k-means clustering is proposed to establish zones for coordination of EV charging based on grid strength and EV owner decision-making to charge per day. Very large-scale distribution networks including the IEEE 123 and 8500 benchmark feeders …

Go to article

Cluster-Based Volt/Var Optimization On A Utility Distribution Feeder With Forecasted Ev Penetration, Steven B. Poore, Grant M. Fischer, Rosemary E. Alden, Evan S. Jones, Aron Patrick, Dan M. Ionel Jun 2025

Cluster-Based Volt/Var Optimization On A Utility Distribution Feeder With Forecasted Ev Penetration, Steven B. Poore, Grant M. Fischer, Rosemary E. Alden, Evan S. Jones, Aron Patrick, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

With significant increase in EV adoption expected in the near future and the associated impacts on power systems, the effect of volt/var optimization (VVO) as the next step to conservation voltage reduction (CVR), requires reevaluation. Implementation of a cluster-based VVO control strategy employs a novel approach with machine learning (ML) load forecasting to reduce device adjustments through k-means clustering of contiguous time steps with similar active power load in which a single adjustment would be sufficient. The cluster-based VVO method is tested on a complex real world utility distribution feeder with 2,018 nodes, 8.65MW peak load, 9 capacitor banks (CBs), …

Go to article

Hybrid Fea And Meta-Modeling For De Optimization Of A Pm Stator-Excited Motor With A Reluctance Rotor, Oluwaseun A. Badewa, Dan M. Ionel Jun 2025

Hybrid Fea And Meta-Modeling For De Optimization Of A Pm Stator-Excited Motor With A Reluctance Rotor, Oluwaseun A. Badewa, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents an innovative method for designing high-performance electric motors by integrating machine learning (ML) based meta-modeling with a differential evolution (DE) optimization algorithm. The approach utilizes finite element analysis (FEA) data to train the ML meta-model, allowing for efficient optimization of high-power-density machines, such as the reluctance rotor and permanent magnet (PM) stator combined excitation motor, which is characterized by nonlinearities. The meta-modeling process employs an Artificial Neural Network (ANN) with 3 hidden layers and uses the motor’s geometrical variables as inputs. The accuracy of the meta-model in capturing the nonlinear relationships between design parameters, core losses, and …

Go to article

Three And Two Phase Rotating Field Inductive Couplers For Wireless Power Transfer With One Phase Per Layer Windings, Donovin D. Lewis, Lucas Gastineau, Omer Onar, Dan M. Ionel Jun 2025

Three And Two Phase Rotating Field Inductive Couplers For Wireless Power Transfer With One Phase Per Layer Windings, Donovin D. Lewis, Lucas Gastineau, Omer Onar, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Multiphase inductive wireless charging coils have been proposed recently to improve coupler surface power density, reduce component stress and size, and provide near-constant power delivery to charge mobile electric systems. Several aspects for the fundamental characterization of multiphase coils are explored up to six phases including approximate mutual inductance with size and turn variation, induced voltage, and output power estimation. The relative component stress and size of passive components for resonant operation are compared between the multiphase variants. A combination of an experimentally validated 3D electromagnetic finite element analysis (FEA) and power electronic co-simulations are used to validate the estimated …

Go to article

Cryogenic Thermal System For Coreless Axial Flux Pm Machine With Litz Wire Winding For Electric Aircraft Propulsion, Matin Vatani, Chaianan Sailabada, Philippe Masson, Juan C. Ordonez, Dan M. Ionel Jun 2025

Cryogenic Thermal System For Coreless Axial Flux Pm Machine With Litz Wire Winding For Electric Aircraft Propulsion, Matin Vatani, Chaianan Sailabada, Philippe Masson, Juan C. Ordonez, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Electric machines for aviation demand ultra-efficient, compact designs with high specific power density, which can be achieved through advancements in both electromagnetic design and thermal management. This paper proposes and investigates an integrated thermal management approach for a coreless stator axial flux permanent magnet (AFPM) machine featuring a double-sided Halbach array PM rotor, two stators, and an aluminum nitride (AlN) cold plate positioned between the stators for direct cooling. The cold plate incorporates internal serpentine channels for liquid hydrogen circulation. The electromagnetic performance and efficiency of the machine at cryogenic temperatures are assessed using temperature-dependent data from the literature, indicating …

Go to article

Next Last