Matching Items (61)
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- All Subjects: Electrical Engineering
- Creators: Yu, Hongbin
Description
The integration of Distributed Energy Resources (DER), including wind energy and photovoltaic (PV) panels, into power systems, increases the potential for events that could lead to outages and cascading failures. This risk is heightened by the limited dynamic information in energy grid datasets, primarily due to sparse Phasor Measurement Units (PMUs) placement. This data quality issue underscores the need for effective methodologies to manage these challenges. One significant challenge is the data gaps in low-resolution (LR) data from RTU and smart meters, hindering robust machine learning (ML) applications. To address this, a systematic approach involves preparing data effectively and designing efficient event detection methods, utilizing both intrinsic physics and extrinsic correlations from power systems. The process begins by interpolating LR data using high-resolution (HR) data, aiming to create virtual PMUs for improved grid management. Current interpolation methods often overlook extrinsic spatial-temporal correlations and intrinsic governing equations like Ordinary Differential Equations (ODEs) or Differential Algebraic Equations (DAEs). Physics-Informed Neural Networks (PINNs) are used for this purpose, though they face challenges with limited LR samples. The solution involves exploring the embedding space governed by ODEs/DAEs, generating extrinsic correlations for initial LR data imputation, and enforcing intrinsic physical constraints for refinement. After data preparation, event data dimensions such as spatial, temporal, and measurement categories are recovered in a tensor. To prevent overfitting, common in traditional ML methods, tensor decomposition is used. This technique merges intrinsic and physical information across dimensions, yielding informative and compact feature vectors for efficient feature extraction and learning in event detection. Lastly, in grids with insufficient data, knowledge transfer from grids with similar event patterns is a viable solution. This involves optimizing projected and transferred vectors from tensor decomposition to maximize common knowledge utilization across grids. This strategy identifies common features, enhancing the robustness and efficiency of ML event detection models, even in scenarios with limited event data.
ContributorsMa, Zhihao (Author) / Weng, Yang (Thesis advisor) / Wu, Meng (Committee member) / Yu, Hongbin (Committee member) / Matavalam, Amarsagar Reddy Ramapuram (Committee member) / Arizona State University (Publisher)
Created2023
Description
Although the increasing penetration of electric vehicles (EVs) has reduced the emissionof the greenhouse gas caused by vehicles, it would lead to serious congestion
on-road and in charging stations. Strategic coordination of EV charging would benefit
the transportation system. However, it is difficult to model a congestion game,
which includes choosing charging routes and stations. Furthermore, conventional algorithms
cannot balance System Optimization and User Equilibrium, which can cause
a huge waste to the whole society. To solve these problems, this paper shows (1) a
congestion game setup to optimize and reveal the relationship between EV users,
(2) using ε – Nash Equilibrium to reduce the inefficient impact from the self-minded
the behavior of the EV users, and (3) finding the relatively optimal solution to approach
Pareto-Optimal solution. The proposed method can reduce more total EVs charging
time and most EV users’ charging time than existing methods. Numerical simulations
demonstrate the advantages of the new method compared to the current methods.
ContributorsYu, Hao (Author) / Weng, Yang (Thesis advisor) / Yu, Hongbin (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2022
Description
The strong demand for the advancing of Moore’s law on device size scaling down has accelerated the miniaturization of passive devices. Among these important electronic components, inductors are facing challenges because the inductance value, which is strongly dependent on the coil number for the air core inductor case, will be sacrificed when the size is shrinking. Adding magnetic core is one of the solutions due to its enhancement of inductance density but it will also add complexity to the fabrication process, and the core loss induced by the eddy current at high frequency is another drawback. In this report, the output of this research will be presented, which has three parts. In the first part, the CoZrTaB thin films are sputtered on different substrates and characterized comprehensively. The laminated CoZrTaB thin films have been also investigated, showing low coercivity and anisotropy field on both Si and polyimide substrates. Also, the different process conditions that could affect the magnetic properties are investigated.
In the second part, Ansys Maxwell software is used to optimize the lamination profile and the magnetic core inductor structure. The measured M-H loop is imported to improve the simulation accuracy.
In the third part, a novel method to fabricate the magnetic core inductors on flexible substrates is proposed. The sandwich magnetic core inductor is fabricated and assembled with flipchip bonder. The measurement result shows that this single-turn magnetic core inductor can achieve up to 24% inductance enhancement and quality factor of 7.42. The super low DC resistance (< 60 mΩ) proves that it is a good candidate to act as the passive component in the power delivery module and the use of polyimide-based substrate extends its compatibility to more packaging form factors.
ContributorsWu, Yanze (Author) / Yu, Hongbin (Thesis advisor) / Chickamenahalli, Shamala (Committee member) / Rizzo, Nicholas (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2022
Description
Voltage Source Inverter (VSI) is an integral component that converts DC voltage to AC voltage suitable for driving the electric motor in Electric Vehicles/Hybrid Electric Vehicles (EVs/HEVs) and integration with electric grid in grid-connected photovoltaic (PV) converter. Performance of VSI is significantly impacted by the type of Pulse Width Modulation (PWM) method used.In this work, a new PWM method called 240° Clamped Space Vector PWM (240CPWM) is studied extensively. 240CPWM method has the major advantages of clamping a phase to the positive or negative rail for 240° in a fundamental period, clamping of two phases simultaneously at any given instant, and use of only active states, completely eliminating the zero states. These characteristics lead to a significant reduction in switching losses of the inverter and lower DC link capacitor current stress as compared to Conventional Space Vector PWM. A unique six pulse dynamically varying DC link voltage is required for 240CPWM instead of constant DC link voltage to maintain sinusoidal output voltage. Voltage mode control of DC-DC stage with Smith predictor is developed for shaping the dynamic DC link voltage that meets the requirements for fast control.
Experimental results from a 10 kW hardware prototype with 10 kHz switching frequency validate the superior performance of 240CPWM in EV/HEV traction inverters focusing on loss reduction and DC link capacitor currents. Full load efficiency with the proposed 240CPWM for the DC-AC stage even with conventional Silicon devices exceeds 99%.
Performance of 240CPWM is evaluated in three phase grid-connected PV converter. It is verified experimentally that 240CPWM performs well under adverse grid conditions like sag/swell and unbalance in grid voltages, and under a wide range of power factor. Undesired low frequency harmonics in inverter currents are minimized using the Harmonic Compensator that results in Total Harmonic Distortion (THD) of 3.5% with 240CPWM in compliance with grid interconnection standards. A new, combined performance index is proposed to compare the performance of different PWM schemes in terms of switching loss, THD, DC link current stress, Common Mode Voltage and leakage current. 240CPWM achieves the best value for this index among the PWM methods studied.
ContributorsQamar, Haleema (Author) / Ayyanar, Raja (Thesis advisor) / Yu, Hongbin (Committee member) / Lei, Qin (Committee member) / Weng, Yang (Committee member) / Arizona State University (Publisher)
Created2022
Description
The performance of voltage source inverter (VSI) in terms of output waveform quality, conversion efficiency and common mode noise depends greatly on the pulse width modulation (PWM) method. In this work, a low-loss space vector PWM i.e., 240°-clamped space vector PWM (240CPWM) is proposed to improve the performance of VSIs in electric/hybrid electric vehicles (EV/HEVs) and grid connected photovoltaic (PV) systems. The salient features of 240CPWM include 240° clamping of each phase pole to positive or negative DC bus in a fundamental cycle ensuring that switching losses are reduced by a factor of seven as compared to conventional space vector PWM (CSVPWM) at unity power factor. Zero states are completely eliminated and only two nearest active states are used ensuring that there is no penalty in terms of total harmonic distortion (THD) in line current. The THD of the line current is analyzed using the notion of stator flux ripple and compared with conventional and discontinuous PWM method. Discontinuous PWM methods achieve switching loss reduction at the expense of higher THD while 240CPWM achieves a much greater loss reduction without impacting the THD. The analysis and performance of 240CPWM are validated on a 10 kW two-stage experimental prototype.
Common mode voltage (CMV) and leakage current characteristics of 240CPWM are analyzed in detail. It is shown analytically that 240CPWM reduces the CMV and leakage current as compared to other PWM methods while simultaneously reducing the switching loss and THD. Experimental results from a 10-kW hardware prototype conform to the analytical discussions and validate the superior performance of 240CPWM.
240CPWM requires a six-pulse dynamic DC link voltage that introduces low frequency harmonics in DC input current and/or AC line currents that can affect maximum power point tracking, battery life or THD in line current. Four topologies have been proposed to minimize the low frequency harmonics in input and line currents in grid-connected PV system with 240CPWM.
In order to achieve further benefits in terms of THD and device stress reduction, 240CPWM is extended to three-level inverters. The performance metrics such as THD and switching loss for 240CPWM are analyzed in three-level inverter.
ContributorsQamar, Hafsa (Author) / Ayyanar, Raja (Thesis advisor) / Yu, Hongbin (Committee member) / Lei, Qin (Committee member) / Weng, Yang (Committee member) / Arizona State University (Publisher)
Created2022
Description
A remarkable phenomenon in contemporary physics is quantum scarring in classically chaoticsystems, where the wave functions tend to concentrate on classical periodic orbits. Quantum
scarring has been studied for more than four decades, but the problem of efficiently detecting
quantum scars has remained to be challenging, relying mostly on human visualization of
wave function patterns. This paper develops a machine learning approach to detecting
quantum scars in an automated and highly efficient manner. In particular, this paper exploits Meta
learning. The first step is to construct a few-shot classification algorithm, under the
requirement that the one-shot classification accuracy be larger than 90%. Then propose a
scheme based on a combination of neural networks to improve the accuracy. This paper shows that
the machine learning scheme can find the correct quantum scars from thousands images of
wave functions, without any human intervention, regardless of the symmetry of the underlying
classical system. This will be the first application of Meta learning to quantum systems. Interacting spin networks are fundamental to quantum computing. Data-based tomography oftime-independent spin networks has been achieved, but an open challenge is to ascertain the
structures of time-dependent spin networks using time series measurements taken locally
from a small subset of the spins. Physically, the dynamical evolution of a spin network under
time-dependent driving or perturbation is described by the Heisenberg equation of motion.
Motivated by this basic fact, this paper articulates a physics-enhanced machine learning framework
whose core is Heisenberg neural networks. This paper demonstrates that, from local measurements, not only the local Hamiltonian can be recovered but the Hamiltonian reflecting the interacting structure of the whole system can
also be faithfully reconstructed. Using Heisenberg neural machine on spin networks of a
variety of structures. In the extreme case where measurements are taken from only one spin,
the achieved tomography fidelity values can reach about 90%. The developed machine
learning framework is applicable to any time-dependent systems whose quantum dynamical
evolution is governed by the Heisenberg equation of motion.
ContributorsHan, Chendi (Author) / Lai, Ying-Cheng (Thesis advisor) / Yu, Hongbin (Committee member) / Dasarathy, Gautam (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2022
Description
Machine learning (ML) and deep learning (DL) has become an intrinsic part of multiple fields. The ability to solve complex problems makes machine learning a panacea. In the last few years, there has been an explosion of data generation, which has greatly improvised machine learning models. But this comes with a cost of high computation, which invariably increases power usage and cost of the hardware. In this thesis we explore applications of ML techniques, applied to two completely different fields - arts, media and theater and urban climate research using low-cost and low-powered edge devices. The multi-modal chatbot uses different machine learning techniques: natural language processing (NLP) and computer vision (CV) to understand inputs of the user and accordingly perform in the play and interact with the audience. This system is also equipped with other interactive hardware setups like movable LED systems, together they provide an experiential theatrical play tailored to each user. I will discuss how I used edge devices to achieve this AI system which has created a new genre in theatrical play. I will then discuss MaRTiny, which is an AI-based bio-meteorological system that calculates mean radiant temperature (MRT), which is an important parameter for urban climate research. It is also equipped with a vision system that performs different machine learning tasks like pedestrian and shade detection. The entire system costs around $200 which can potentially replace the existing setup worth $20,000. I will further discuss how I overcame the inaccuracies in MRT value caused by the system, using machine learning methods. These projects although belonging to two very different fields, are implemented using edge devices and use similar ML techniques. In this thesis I will detail out different techniques that are shared between these two projects and how they can be used in several other applications using edge devices.
ContributorsKulkarni, Karthik Kashinath (Author) / Jayasuriya, Suren (Thesis advisor) / Middel, Ariane (Thesis advisor) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2021
Description
Since the invention of the automobile, engineers have been designing and making newer and newer improvements to them in order to provide customers with safer, faster, more reliable, and more comfortable vehicles. With each new generation, new technology can be seen being introduced into mainstream products, one of which that is currently being pushed is that of autonomy. Established brand manufacturers and small research teams have been dedicated for years to find a way to make the automobile autonomous with none of them being able to confidently answer that they have found a solution. Among the engineering community there are two schools of thought when solving this issue: camera and LiDAR; some believe that only cameras and computer vision are required while other believe that LiDAR is the solution. The most optimal case is to use both cameras and LiDAR’s together in order to increase reliability and ensure data confidence. Designers are reluctant to use LiDAR systems due to their massive weight, cost, and complexity; with too many moving components, these systems are very bulky and have multiple costly, moving parts that eventually need replacement due to their constant motion. The solution to this problem is to develop a solid-state LiDAR system which would solve all those issues previously stated and this research takes it one level further and looks into a potential prototype for a solid-state camera and Lidar package. Currently no manufacturer offers a system that contains a solid-state LiDAR system and a solid-state camera with computing capabilities, all manufacturers provided either just the camera, just the Lidar, or just the computation ability. This design will also use of the shelf COTS parts in order to increase reproducibility for open-source development and to reduce total manufacturing cost. While keeping costs low, this design is also able to keep its specs and performance on par with that of a well-used commercial product, the Velodyne VL50.
ContributorsEltohamy, Gamal (Author) / Yu, Hongbin (Thesis advisor) / Goryll, Michael (Committee member) / Allee, David (Committee member) / Arizona State University (Publisher)
Created2024
Description
Antenna arrays are widely used in wireless communication, radar, remote sensing, and other fields. Compared to traditional linear antenna arrays, novel nonlinear antenna arrays have fascinating advantages in terms of structural simplicity, lower cost, wider bandwidth, faster scanning speed, and lower side-lobe levels. This dissertation explores a novel design of a phased array antenna with an augmented scanning range, aiming to establish a clear connection between mathematical principles and practical circuitry. To achieve this goal, the Van der Pol (VDP) model is applied to a single-transistor oscillator to obtain the isolated limit cycle. The coupled oscillators are then integrated into a 1 times 7 coupled phased array, using the Keysight PathWave Advanced Design System (ADS) for tuning and optimization. The VDP model is used for analytic study of bifurcation, quasi-sinusoidal oscillation, quasi-periodic chaos, and oscillator death, while ADS schematics guide engineering implementation and physical fabrication. The coupled oscillators drive cavity-backed antennas, forming a one-dimensional scanning antenna array of 1 times 7. The approaches for increasing the scanning range performance are discussed.
ContributorsZhang, Kaiyue (Author) / Pan, George (Thesis advisor) / Yu, Hongbin (Committee member) / Aberle, James (Committee member) / Palais, Joseph (Committee member) / Arizona State University (Publisher)
Created2023
Description
Graphene has been extensively researched for both scientific and technological interests since its first isolation from graphite. The excellent transport properties and long spin diffusion length of graphene make it a promising material for electronic and spintronic device applications. This dissertation deals with the optimization of magnetic field sensing in graphene and the realization of nanoparticle induced ferromagnetism in graphene towards spintronic device applications.
Graphene has been used as a channel material for magnetic sensors demonstrating the potential for very high sensitivities, especially for Hall sensors, due to its extremely high mobility and low carrier concentration. However, the two-carrier nature of graphene near the charge neutrality point (CNP) causes a nonlinearity issue for graphene Hall sensors, which limits useful operating ranges and has not been fully studied. In this dissertation, a two-channel model was used to describe the transport of graphene near the CNP. The model was carefully validated by experiments and then was used to explore the optimization of graphene sensor performance by tuning the gate operating bias under realistic constraints on linearity and power dissipation.
The manipulation of spin in graphene that is desired for spintronic applications is limited by its weak spin-orbit coupling (SOC). Proximity induced ferromagnetism (PIFM) from an adjacent ferromagnetic insulator (FMI) provides a method for enhancing SOC in graphene without degrading its transport properties. However, suitable FMIs are uncommon and difficult to integrate with graphene. In this dissertation, PIFM in graphene from an adjacent Fe3O4 magnetic nanoparticle (MNP) array was demonstrated for the first time. Observation of the anomalous Hall effect (AHE) in the device structures provided the signature of PIFM. Comparison of the test samples with different control samples conclusively proved that exchange interaction at the MNP/graphene interface was responsible for the observed characteristics. The PIFM in graphene was shown to persist at room temperature and to be gate-tunable, which are desirable features for electrically controlled spintronic device applications.
The observation of PIFM in the MNP/graphene devices indicates that the spin transfer torque (STT) from spin-polarized current in the graphene can interact with the magnetization of the MNPs. If there is sufficient STT, spin torque oscillation (STO) could be realized in this structure. In this dissertation, three methods were employed to search for signatures of STO in the devices. STO was not observed in our devices, most likely due to the weak spin-polarization for current injected from conventional ferromagnetic contacts to graphene. Calculation indicates that graphene should provide sufficient spin-polarized current for exciting STO in optimized structures that miniaturize the device area and utilize optimized tunnel-barrier contacts for improved spin injection.
Graphene has been used as a channel material for magnetic sensors demonstrating the potential for very high sensitivities, especially for Hall sensors, due to its extremely high mobility and low carrier concentration. However, the two-carrier nature of graphene near the charge neutrality point (CNP) causes a nonlinearity issue for graphene Hall sensors, which limits useful operating ranges and has not been fully studied. In this dissertation, a two-channel model was used to describe the transport of graphene near the CNP. The model was carefully validated by experiments and then was used to explore the optimization of graphene sensor performance by tuning the gate operating bias under realistic constraints on linearity and power dissipation.
The manipulation of spin in graphene that is desired for spintronic applications is limited by its weak spin-orbit coupling (SOC). Proximity induced ferromagnetism (PIFM) from an adjacent ferromagnetic insulator (FMI) provides a method for enhancing SOC in graphene without degrading its transport properties. However, suitable FMIs are uncommon and difficult to integrate with graphene. In this dissertation, PIFM in graphene from an adjacent Fe3O4 magnetic nanoparticle (MNP) array was demonstrated for the first time. Observation of the anomalous Hall effect (AHE) in the device structures provided the signature of PIFM. Comparison of the test samples with different control samples conclusively proved that exchange interaction at the MNP/graphene interface was responsible for the observed characteristics. The PIFM in graphene was shown to persist at room temperature and to be gate-tunable, which are desirable features for electrically controlled spintronic device applications.
The observation of PIFM in the MNP/graphene devices indicates that the spin transfer torque (STT) from spin-polarized current in the graphene can interact with the magnetization of the MNPs. If there is sufficient STT, spin torque oscillation (STO) could be realized in this structure. In this dissertation, three methods were employed to search for signatures of STO in the devices. STO was not observed in our devices, most likely due to the weak spin-polarization for current injected from conventional ferromagnetic contacts to graphene. Calculation indicates that graphene should provide sufficient spin-polarized current for exciting STO in optimized structures that miniaturize the device area and utilize optimized tunnel-barrier contacts for improved spin injection.
ContributorsSong, Guibin (Author) / Kiehl, Richard A. (Committee member) / Yu, Hongbin (Committee member) / Chen, Tingyong (Committee member) / Rizzo, Nicholas D (Committee member) / Arizona State University (Publisher)
Created2019