Engineering Commons^™

Rethinking Wind In Kentucky, Lawrence E. Holloway, Aron Patrick, Dan M. Ionel

Power and Energy Institute of Kentucky Faculty Publications

Recent analyses and developments suggest that wind energy could play a role in Kentucky's future power generation mix. This recent change in outlook for Kentucky wind has been driven by three factors: (1) improved wind turbine technologies, (2) improved economics, and (3) recent analyses showing improved grid reliability due to wind's complementarity to solar power generation.

Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso

Theses and Dissertations--Electrical and Computer Engineering

The emergence of deep learning models and their success in visual object recognition have fueled the medical imaging community's interest in integrating these algorithms to improve medical diagnosis. However, natural images, which have been the main focus of deep learning models and mammograms, exhibit fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions but are generally heavily downsampled to fit these images to deep learning models. Models that handle high-resolution mammograms require many exams and complex architectures. Additionally, spatially resizing mammograms leads to losing discriminative details essential …

Reinventing Integrated Photonic Devices And Circuits For High Performance Communication And Computing Applications, Venkata Sai Praneeth Karempudi

Theses and Dissertations--Electrical and Computer Engineering

The long-standing technological pillars for computing systems evolution, namely Moore's law and Von Neumann architecture, are breaking down under the pressure of meeting the capacity and energy efficiency demands of computing and communication architectures that are designed to process modern data-centric applications related to Artificial Intelligence (AI), Big Data, and Internet-of-Things (IoT). In response, both industry and academia have turned to 'more-than-Moore' technologies for realizing hardware architectures for communication and computing. Fortunately, Silicon Photonics (SiPh) has emerged as one highly promising ‘more-than-Moore’ technology. Recent progress has enabled SiPh-based interconnects to outperform traditional electrical interconnects, offering advantages like high bandwidth density, …

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 …

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 …

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

Theses and Dissertations--Mechanical 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 …

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. …

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 …

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 …

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 …

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 …

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) …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

The Effects Of Reference-Command Preview On The Strategies That Humans Use In Command-Following Tasks, Amelia J. S. Sheffler

Theses and Dissertations--Mechanical Engineering

This thesis presents results from an experiment in which 22 human subjects each interact with a dynamic system 40 times over a one-week period. For each interaction, a subject performs a command-following task, where the reference command is the same for all 22 subjects but different on each trial. The subjects are divided into 2 groups of 11 subjects. One group performs the command-following task without reference-command preview. The other group is provided with 1-s preview of the reference command. The experimental results are used to examine the effects of reference-command preview. For the group with 1-s reference-command preview, the …

Experimental Comparison Of Two Sampled-Data Adaptive Control Algorithms For Rejecting Sinusoidal Disturbances, William Grayson Woods

Theses and Dissertations--Mechanical Engineering

We review two adaptive control algorithms that address the problem of rejecting sinusoids with known frequencies that act on an unknown asymptotically stable linear time-invariant system. We present modifications to the algorithms that address the problems of sensor noise and actuator saturation. We demonstrate the effectiveness of the algorithms and compare the performance of the algorithms via numerical simulation and experimental testing.

Determining Power System Fault Location Using Neural Network Approach, Edward O. Ojini

Theses and Dissertations--Electrical and Computer Engineering

Fault location remains an extremely pivotal feature of the electric power grid as it ensures efficient operation of the grid and prevents large downtimes during fault occurrences. This will ultimately enhance and increase the reliability of the system. Since the invention of the electric grid, many approaches to fault location have been studied and documented. These approaches are still effective and are implemented in present times, and as the power grid becomes even more broadened with new forms of energy generation, transmission, and distribution technologies, continued study on these methods is necessary. This thesis will focus on adopting the artificial …

Formation Control With Bounded Controls And Collision Avoidance: Theory And Application To Quadrotor Unmanned Air Vehicles, Zachary S. Lippay

Theses and Dissertations--Mechanical Engineering

This dissertation presents new results on multi-agent formation control and applies the new control algorithms to quadrotor unmanned air vehicles. First, this dissertation presents a formation control algorithm for double-integrator agents, where the formation is time varying and the agents’ controls satisfy a priori bounds (e.g., the controls accommodate actuator saturation). The main analytic results provide sufficient conditions such that all agents converge to the desired time-varying relative positions with one another and the leader, and have a priori bounded controls (if applicable). We also present results from rotorcraft experiments that demonstrate the algorithm with time-varying formations and bounded controls. …