Electrical and Computer Engineering Commons^™

Co-Simulation Of Electric Power Distribution Systems And Buildings Including Ultra-Fast Hvac Models And Optimal Der Control, Evan S. Jones, Rosemary E. Alden, Huangjie Gong, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Smart homes and virtual power plant (VPP) controls are growing fields of research with potential for improved electric power grid operation. A novel testbed for co-simulation of electric power distribution systems and distributed energy resources (DERs), is employed to evaluate VPP scenarios and propose an optimization procedure. DERs of specific interest include behind-the-meter (BTM) solar photovoltaic (PV) systems as well as heating, ventilation, and air-conditioning (HVAC) systems. Simulation of HVAC systems is enabled by a machine learning procedure that produces ultra-fast models for electric power and indoor temperature of associated buildings that are up to 133 times faster than typical …

Axial Flux Permanent Magnet Vernier Machine With Single-Wound Dual-Stator And Spoke Permanent Magnet Rotor For Electric Vehicle In-Wheel Traction, Ali Mohammadi, Yaser Chulaee, Aaron M. Cramer, Ion G. Boldea, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

In this paper, a dual-stator axial flux permanent magnet machine with a spoke-type rotor for electric vehicle applications is investigated. The rotor PMs are positioned in a spoke-type field-concentrating arrangement, which provides high flux concentration. Two stators—one stator configuration with double-layer wound coils and another without winding—are employed. The usage of single-layer wound coils is possible in this topology, and by using this type of winding it is possible to implement advanced in-slot cooling systems. Analytical and electromagnetic finite element analysis (FEA) studies considering its geometrical and electrical properties have been performed on this machine. The performance indices for the …

Optimal Power Sharing Speed Compensation In On-Road Wireless Ev Charging Systems, Donovin Lewis, Omer Onar, Veda Prakash Galigekere, Mostak Mohammad, Dan Ionel

Electrical and Computer Engineering Graduate Research

Dynamic wireless charging of electric vehicles (EV) is an emerging technology with the potential to address range anxiety and reduce the size of batteries or provide charge sustaining operation. Charging demand for dynamic wireless charging systems (DWCS) varies greatly in response to location specific traffic behaviors including the number and speed of vehicles. This paper highlights the potential reduction of load variation with speed compensation and simulates opportunities to maximize the number of cars charged concurrently through ”power sharing” or altering power output to slower cars while maintaining a maximum delivered energy. An improved sub- minute model for synthetic traffic …

Torque And Power Capabilities Of Coreless Axial Flux Machines With Surface Pms And Halbach Array Rotors, Yaser Chulaee, Donovin Lewis, Matin Vatani, John F. Eastham, Dan Ionel

Power and Energy Institute of Kentucky Faculty Publications

This paper investigates employing a Halbach PM rotor array to increase torque and power density within coreless axial flux permanent magnet (AFPM) machines. Analytical and 2/3-dimensional finite element analysis (FEA) methods are developed to study torque and power capabilities within an example double-rotor, single-stator coreless AFPM machine with a PCB stator. Compared to a surface PM topology of the same mass and volume, employing a Halbach array increases torque density by as much as 30% through increased airgap flux density amplitude. Multiple parametric studies are performed to explore methods of increasing torque and power density while employing Halbach arrays combined …

Design Optimization Of A Direct-Drive Wind Generator With Non-Rare-Earth Pm Flux Intensifying Stator And Reluctance Rotor, Ali Mohammadi, Oluwaseun A. Badewa, Yaser Chulaee, Dan Ionel, Somasundaram Essakiappan, Madhav Manjrekar

Power and Energy Institute of Kentucky Faculty Publications

This paper presents a multi-objective design optimization for a novel direct-drive wind turbine generator. The proposed electric machine topology employs an outer rotor of the reluctance type and a special modular stator including three phase-windings and spoke-type permanent magnets (PMs). Each stator module includes a single coil toroidally wound around the ferromagnetic core. Consecutive stator modules are separated by PMs and include coils belonging to a different phase. An optimization method with three objectives: total power loss, weight, and torque ripple, and with one constraint for a minimum acceptable value for the power factor, is described. The design examples are …

Description Of Reaction And Vibrational Energetics Of Co2-Nh3interaction Using Quantum Computing Algorithms, Manh Tien Nguyen, Yueh-Lin Lee, Dominic Alfonso, Qing Shao, Yuhua Duan

Markey Cancer Center Faculty Publications

CO2 capture is critical to solving global warming. Amine-based solvents are extensively used to chemically absorb CO2. Thus, it is crucial to study the chemical absorption of CO2 by amine-based solvents to better understand and optimize CO2 capture processes. Here, we use quantum computing algorithms to quantify molecular vibrational energies and reaction pathways between CO2 and a simplified amine-based solvent model—NH3. Molecular vibrational properties are important to understanding kinetics of reactions. However, the molecule size correlates with the strength of anharmonicity effect on vibrational properties, which can be challenging to address using classical computing. Quantum computing can help enhance molecular …

Decarbonization Analysis For Thermal Generation And Regionally Integrated Large-Scale Renewables Based On Minutely Optimal Dispatch With A Kentucky Case Study, Donovin Lewis, Aron Patrick, Evan S. Jones, Rosemary E. Alden, Abdullah Al Hadi, Malcolm D. Mcculloch, Dan Ionel

Power and Energy Institute of Kentucky Faculty Publications

Decarbonization of existing electricity generation portfolios with large-scale renewable resources, such as wind and solar photo-voltaic (PV) facilities, is important for a transition to a sustainable energy future. This paper proposes an ultra-fast optimization method for economic dispatch of firm thermal generation using high granularity, one minute resolution load, wind, and solar PV data to more accurately capture the effects of variable renewable energy (VRE). Load-generation imbalance and operational cost are minimized in a multi-objective clustered economic dispatch problem with various generation portfolios, realistic generator flexibility, and increasing levels of VRE integration. The economic feasibility of thermal dispatch scenarios is …

Circulating And Eddy Current Losses In Coreless Axial Flux Pm Machine Stators With Pcb Windings, Yaser Chulaee, Donovin Lewis, Ali Mohammadi, Greg Heins, Dean Patterson, Dan Ionel

Power and Energy Institute of Kentucky Faculty Publications

Printed circuit board (PCB) stators in coreless axial flux permanent magnet (AFPM) machines have been proposed, designed, and studied for use in multiple industries due to their design flexibility and reduction of manufacturing costs, volume, and weight compared to conventional stators. This paper investigates mechanisms and methods of approximating open circuit losses in PCB stators within example wave and spiral winding topologies for a dual rotor, single stator configuration using 3D FEA, analytical hybrid techniques and experiments. The effect of rotor magnet shape, end winding, and active conductor geometry on eddy currents is studied, and some mitigation techniques are proposed. …

Parallel Real Time Rrt*: An Rrt* Based Path Planning Process, David Yackzan

Theses and Dissertations--Mechanical and Aerospace Engineering

This thesis presents a new parallelized real-time path planning process. This process is an extension of the Real-Time Rapidly Exploring Random Trees* (RT-RRT*) algorithm developed by Naderi et al in 2015 [1]. The RT-RRT* algorithm was demonstrated on a simulated two-dimensional dynamic environment while finding paths to a varying target state. We demonstrate that the original algorithm is incapable of running at a sufficient rate for control of a 7-degree-of-freedom (7-DoF) robotic arm while maintaining a path planning tree in 7 dimensions. This limitation is due to the complexity of maintaining a tree in a high-dimensional space and the network …

Generalizable And Adaptable Data-Driven Methods For Overcoming Barriers To Practical Industrial Condition Monitoring, Matthew B. Russell

Theses and Dissertations--Electrical and Computer Engineering

The future of smart manufacturing relies on predictive maintenance systems that intelligently minimize expensive downtime through timely assessment of machine condition. Deep Learning (DL) has achieved excellent performance in industrial condition monitoring experiments, but the constraints of the manufacturing environment prevent many algorithms from being practically deployed on the factory floor. Ubiquitous sensing from online machines generates high velocity data streams that require new techniques for efficient transmission and storage. Despite these ever-increasing data lakes, many applications still lack the data needed for training DL fault diagnosis and wear tracking models since most data is unlabeled and only from nominal …

Unmanned Aircraft Systems For Precision Meteorology: An Analysis Of Gnss Position Measurement Error And Embedded Sensor Development, Karla S. Ladino

Theses and Dissertations--Biosystems and Agricultural Engineering

The overarching objective of this research was to enhance our comprehension of the three-dimensional precision of meteorological measurements obtained using small unmanned aircraft systems (UAS). Two complimentary experiments were conducted to achieve this objective.

The first experiment entailed the development and implementation of a system to determine the global navigation satellite system (GNSS) position accuracy on a UAS platform. This system was utilized to assess the static and dynamic accuracy of L1 and L1/L2 GNSS receivers in real-time kinematic (RTK) and non-RTK fix modes. Adjusted two-sample t-tests revealed significant differences in horizontal and vertical error between RTK and non-RTK receivers …

Life Cycle Assessment Of Air Classification As A Sulfur Mitigation Technology In Pine Residue Feedstocks, Ashlee Edmonson

Theses and Dissertations--Biosystems and Agricultural Engineering

Sulfur accumulation during biofuel production is pollutive, toxic to conversion catalysts, and causes the premature breakdown of processing equipment. Air classification is an effective preprocessing technology for ash and sulfur removal from biomass feedstocks. A life cycle assessment (LCA) sought to understand the environmental impacts of implementing air classification as a sulfur-mitigation technique for pine residues. Energy demand and material balance for preprocessing were simulated using SimaPro and the Argonne National Laboratory’s GREET model, specifically focusing on comparing the global warming potential (GWP) of grid electricity versus bioelectricity scenarios. Overall, the grid electricity scenario had a GWP impact over 7 …

Peer-To-Peer Energy Trading In Smart Residential Environment With User Behavioral Modeling, Ashutosh Timilsina

Theses and Dissertations--Computer Science

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid.

Trading energy among users in a decentralized fashion has been referred …

Building Energy Modeling And Studies Of Electric Power Distribution Systems With Distributed Energy Resources, Evan S. Jones

Theses and Dissertations--Electrical and Computer Engineering

There is significant opportunity for savings in energy and investment from improved performance of electric Power Distribution Systems (PDSs) through optimal planning and operation of conventional voltage-controlling devices. Novel multi-step model conversion and optimal capacitor planning (OCP) procedures are proposed for large-scale utility PDSs and are exemplified with an existing utility circuit of approximately 4,000 buses. Simulated optimal control and operation is achieved with a cluster-based approach that utilizes load-forecasting to minimize equipment degradation by intelligently dispersing device setting adjustments over time such that they remain most applicable. Improved performance may also be achieved through smart building technologies and Virtual …

Optimal Design Of Special High Torque Density Electric Machines Based On Electromagnetic Fea, Murat G. Kesgin

Theses and Dissertations--Electrical and Computer Engineering

Electric machines with high torque density are essential for many low-speed direct-drive systems, such as wind turbines, electric vehicles, and industrial automation. Permanent magnet (PM) machines that incorporate a magnetic gearing effect are particularly useful for these applications due to their potential for achieving extremely high torque density. However, when the number of rotor polarities is increased, there is a corresponding need to increase the number of stator slots and coils proportionally. This can result in manufacturing challenges. A new topology of an axial-flux vernier-type machine of MAGNUS type has been presented to address the mentioned limitation. These machines can …

A Phase Change Memory And Dram Based Framework For Energy-Efficient And High-Speed In-Memory Stochastic Computing, Supreeth Mysore

Theses and Dissertations--Electrical and Computer Engineering

Convolutional Neural Networks (CNNs) have proven to be highly effective in various fields related to Artificial Intelligence (AI) and Machine Learning (ML). However, the significant computational and memory requirements of CNNs make their processing highly compute and memory-intensive. In particular, the multiply-accumulate (MAC) operation, which is a fundamental building block of CNNs, requires enormous arithmetic operations. As the input dataset size increases, the traditional processor-centric von-Neumann computing architecture becomes ill-suited for CNN-based applications. This results in exponentially higher latency and energy costs, making the processing of CNNs highly challenging.

To overcome these challenges, researchers have explored the Processing-In Memory (PIM) …

Establishing The Foundation To Robotize Complex Welding Processes Through Learning From Human Welders Based On Deep Learning Techniques, Rui Yu

Theses and Dissertations--Electrical and Computer Engineering

As the demand for customized, efficient, and high-quality production increases, traditional manufacturing processes are transforming into smart manufacturing with the aid of advancements in information technology, such as cyber-physical systems (CPS), the Internet of Things (IoT), big data, and artificial intelligence (AI). The key requirement for integration with these advanced information technologies is to digitize manufacturing processes to enable analysis, control, and interaction with other digitized components. The integration of deep learning algorithm and massive industrial data will be critical components in realizing this process, leading to enhanced manufacturing in the Future of Work at the Human-Technology Frontier (FW-HTF).

This …

Application Of Conventional Feedforward And Deep Neural Networks To Power Distribution System State Estimation And State Forecasting, James Paul Carmichael

Theses and Dissertations--Electrical and Computer Engineering

Classical neural networks such as feedforward multilayer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. This research investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to mitigate challenges in power distribution system state estimation and forecasting based upon conventional analytic methods. The ability of MLPs to perform regression to perform power system state estimation will be investigated. MLPs are considered based upon their promise to learn …

Modeling The Early Visual System, Nicholas Lanning

Theses and Dissertations--Electrical and Computer Engineering

There are two encoding schema present in simple cells in the early visual system of vertebrates: the retinal simple cells activate highly when the receptive field contains a center surround stimulus, while the primary visual cortex’s (V1) simple cells activate highly when the receptive field contains visual edges. Work has been done in the past to enforce constraints on visual machine learning such that the retinal or V1 encoding is learned, but this work is often done to emulate retinal and V1 encoding in a vacuum. Recent work using convolutional neural networks focuses on anatomical constraints along with a supervised …

Surface Properties, Work Function, And Thermionic Electron Emission Characterization Of Materials For Next-Generation Dispenser Cathodes, Antonio Mantica

Theses and Dissertations--Chemical and Materials Engineering

A dispenser cathode’s ability to thermionically emit electrons is highly dependent on its material properties, especially those of the surface. Understanding the relationship between surface properties and electron emission, therefore, is vital to reach the next generation of the many vacuum electron devices (VEDs) that rely on the physics of electron emission. In the past century, many techniques have been developed to characterize material surfaces and quantify thermionic emission. These techniques are based on a wide range of different physical phenomena, including measuring photoemission via the photoelectric effect, measuring the electrostatic potential between metals in electrical contact, and current collection …

Solid-State Electrolytes For Long Duration Molten Sodium Batteries, Ryan C. Hill

Theses and Dissertations--Chemical and Materials Engineering

Rechargeable batteries have become a staple of modern society, driving the development and expansion of portable electronics and electric vehicles. The long lifetime and high energy density provided by batteries have made them excellent candidates for use in the emerging field of long duration, grid-scale energy storage. However, traditional battery chemistries, such as Li-ion, are not an ideal solution for grid-scale storage, due to high-cost raw materials that are often sourced from volatile markets. Sodium-based batteries are a promising alternative for long duration storage, owing to the domestic abundance of raw materials and energy densities that nearly rival that of …

Digital Twin For Hvac Load And Energy Storage Based On A Hybrid Ml Model With Cta-2045 Controls Capability, Rosemary E. Alden, Evan S. Jones, Huangjie Gong, Abdullah Al Hadi, Dan Ionel

Power and Energy Institute of Kentucky Faculty Publications

Building modeling, specifically heating, ventilation, and air conditioning (HVAC) load and equivalent energy storage calculations, represent a key focus for decarbonization of buildings and smart grid controls. Widely used white box models, due to their complexity, are too computationally intensive to be employed in high resolution distributed energy resources (DER) platforms without simulation time delays. In this paper, an ultra-fast one-minute resolution Hybrid Machine Learning Model (HMLM) is proposed as part of a novel procedure to replicate white box models as an alternative to widespread experimental big data collection. Synthetic output data from experimentally calibrated EnergyPlus models for three existing …

Low-Cost Compact Optical Spectroscopy And Novel Spectroscopic Algorithm For Point-Of-Care Real-Time Monitoring Of Nanoparticle Delivery In Biological Tissue Models, Zahid Hasan, Jing Yan, Zhongchao Yi, Madison O. Korfhage, Sheng Tong, Caigang Zhu

Markey Cancer Center Faculty Publications

Objective: Real-time monitoring of nanoparticle delivery in biological models is essential to optimize nanoparticle- mediated therapies. However, few techniques are available for convenient real-time monitoring of nanoparticle concentrations in tissue samples. This work reported novel optical spectroscopic approaches for low-cost point-of-care real-time quantification of nanoparticle concentrations in biological tissue samples.

Methods: Fiber probe measured diffuse reflectance can be described with a simple analytical model by introducing an explicit dependence on the reduced scattering coefficient. Relying on this, the changes on the inverse of diffuse reflectance are proportional to absorption change when the scattering perturbation is negligible. We developed this model …

Forecast Of Community Total Electric Load And Hvac Component Disaggregation Through A New Lstm-Based Method, Huangjie Gong, Rosemary E. Alden, Aron Patrick, Dan Ionel

Power and Energy Institute of Kentucky Faculty Publications

The forecast and estimation of total electric power demand of a residential community, its baseload, and its heating ventilation and air-conditioning (HVAC) power component, which represents a very large portion of a community electricity usage, are important enablers for optimal energy controls and utility planning. This paper proposes a method that employs machine learning in a multi-step integrated approach. An LSTM model for total electric power at the main circuit feeder is trained using historic multi-year hourly data, outdoor temperature, and solar irradiance. New key temperature indicators, TmHAVC, corresponding to the standby zero-power operation for HVAC systems for summer cooling …

Prostacyclin Promotes Degenerative Pathology In A Model Of Alzheimer’S Disease, Tasha R. Womack, Craig T. Vollert, Odochi Ohia-Nwoko, Monika Schmitt, Saghi Montazari, Tina L. Beckett, David Mayerich, M. Paul Murphy, Jason L. Eriksen

Molecular and Cellular Biochemistry Faculty Publications

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that is the most common form of dementia in aged populations. A substantial amount of data demonstrates that chronic neuroinflammation can accelerate neurodegenerative pathologies. In AD, chronic neuroinflammation results in the upregulation of cyclooxygenase and increased production of prostaglandin H2, a precursor for many vasoactive prostanoids. While it is well-established that many prostaglandins can modulate the progression of neurodegenerative disorders, the role of prostacyclin (PGI2) in the brain is poorly understood. We have conducted studies to assess the effect of elevated prostacyclin biosynthesis in a mouse model of AD. Upregulated prostacyclin expression …

Recent Advances Of Wind-Solar Hybrid Renewable Energy Systems For Power Generation: A Review, Pranoy Roy, Jiangbiao He, Tiefu Zhao, Yash Veer Singh

Electrical and Computer Engineering Faculty Publications

A hybrid renewable energy source (HRES) consists of two or more renewable energy sources, such as wind turbines and photovoltaic systems, utilized together to provide increased system efficiency and improved stability in energy supply to a certain degree. The objective of this study is to present a comprehensive review of wind-solar HRES from the perspectives of power architectures, mathematical modeling, power electronic converter topologies, and design optimization algorithms. Since the uncertainty of HRES can be reduced further by including an energy storage system, this paper presents several hybrid energy storage system coupling technologies, highlighting their major advantages and disadvantages. Various …

Evaluation Of The Accuracy Of Different Pv Estimation Models And The Effect Of Dust Cleaning: Case Study A 103 Mw Pv Plant In Jordan, Loiy Al-Ghussain, Moath Abu Subaih, Andres Annuk

Mechanical Engineering Graduate Research

The estimation of PV production has been widely investigated previously, where many empirical models have been proposed to account for wind and soiling effects for specific locations. However, the performance of these models varies among the investigated sites. Hence, it is vital to assess and evaluate the performance of these models and benchmark them against the common PV estimation model that accounts only for the ambient temperature. Therefore, this study aims to evaluate the accuracy and performance of four empirical wind models considering the soiling effect, and compare them to the standard model for a 103 MW PV plant in …

Highly-Individualized Physical Therapy Instruction Beyond The Clinic Using Wearable Inertial Sensors, Samir A. Rawashdeh, Ella Reimann, Timothy L. Uhl

Physical Therapy Faculty Publications

Musculoskeletal conditions, often requiring rehabilitation, affect one-third of the U.S. population annually. This paper presents rehabilitation assistive technology that includes body-worn motion sensors and a mobile application that extends the reach of a physical rehabilitation specialist beyond the clinic to ensure that home exercises are performed with the same precision as under clinical supervision. Assisted by a specialist in the clinic, the wearable sensors and user interface developed allow the capture of individualized exercises unique to the patient’s physical abilities. Beyond the clinical setting, the system can assist patients by providing real-time corrective feedback to repeat these exercises through a …

Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao

Electrical and Computer Engineering Faculty Publications

Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks …

Application Of Deep Neural Networks To Distribution System State Estimation And Forecasting, James P. Carmichael, Yuan Liao

Electrical and Computer Engineering Faculty Publications

Classical neural networks such as feedforward multi-layer perceptron models (MLPs) are well established as universal approximators and as such, show promise in applications such as static state estimation in power transmission systems. The dynamic nature of distributed generation (i.e. solar and wind), vehicle to grid technology (V2G) and false data injection attacks (FDIAs), may pose significant challenges to the application of classical MLPs to state estimation (SE) and state forecasting (SF) in power distribution systems. This paper investigates the application of conventional neural networks (MLPs) and deep learning based models such as convolutional neural networks (CNNs) and long-short term networks …