Matching Items (598)
Filtering by
- All Subjects: Electrical Engineering
- Genre: Doctoral Dissertation
Description
Event identification is increasingly recognized as crucial for enhancing the reliability, security, and stability of the electric power system. With the growing deployment of Phasor Measurement Units (PMUs) and advancements in data science, there are promising opportunities to explore data-driven event identification via machine learning classification techniques. This dissertation explores the potential of data-driven event identification through machine learning classification techniques. In the first part of this dissertation, using measurements from multiple PMUs, I propose to identify events by extracting features based on modal dynamics. I combine such traditional physics-based feature extraction methods with machine learning to distinguish different event types.Using the obtained set of features, I investigate the performance of two well-known classification models, namely, logistic regression (LR) and support vector machines (SVM) to identify generation loss and line trip events in two datasets. The first dataset is obtained from simulated events in the Texas 2000-bus synthetic grid. The second is a proprietary dataset with labeled events obtained from a large utility in the USA.
My results indicate that the proposed framework is promising for identifying the two types of events in the supervised setting. In the second part of the dissertation, I use semi-supervised learning techniques, which make use of both labeled and unlabeled samples.I evaluate three categories of classical semi-supervised approaches: (i) self-training, (ii) transductive support vector machines (TSVM), and (iii) graph-based label spreading (LS) method. In particular, I focus on the identification of four event classes i.e., load loss, generation loss, line trip, and bus fault. I have developed and publicly shared a comprehensive Event Identification package which consists of three aspects: data generation, feature extraction, and event identification with limited labels using semi-supervised methodologies. Using this package, I generate eventful PMU data for the South Carolina 500-Bus synthetic network. My evaluation confirms that the integration of additional unlabeled samples and the utilization of LS for pseudo labeling surpasses the outcomes achieved by the self-training and TSVM approaches. Moreover, the LS algorithm consistently enhances the performance of all classifiers more robustly.
ContributorsTaghipourbazargani, Nima (Author) / Kosut, Oliver (Thesis advisor) / Sankar, Lalitha (Committee member) / Pal, Anamitra (Committee member) / Dasarathy, Gautam (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation centers on the development of Bayesian methods for learning differ- ent types of variation in switching nonlinear gene regulatory networks (GRNs). A new nonlinear and dynamic multivariate GRN model is introduced to account for different sources of variability in GRNs. The new model is aimed at more precisely capturing the complexity of GRN interactions through the introduction of time-varying kinetic order parameters, while allowing for variability in multiple model parameters. This model is used as the drift function in the development of several stochastic GRN mod- els based on Langevin dynamics. Six models are introduced which capture intrinsic and extrinsic noise in GRNs, thereby providing a full characterization of a stochastic regulatory system. A Bayesian hierarchical approach is developed for learning the Langevin model which best describes the noise dynamics at each time step. The trajectory of the state, which are the gene expression values, as well as the indicator corresponding to the correct noise model are estimated via sequential Monte Carlo (SMC) with a high degree of accuracy. To address the problem of time-varying regulatory interactions, a Bayesian hierarchical model is introduced for learning variation in switching GRN architectures with unknown measurement noise covariance. The trajectory of the state and the indicator corresponding to the network configuration at each time point are estimated using SMC. This work is extended to a fully Bayesian hierarchical model to account for uncertainty in the process noise covariance associated with each network architecture. An SMC algorithm with local Gibbs sampling is developed to estimate the trajectory of the state and the indicator correspond- ing to the network configuration at each time point with a high degree of accuracy. The results demonstrate the efficacy of Bayesian methods for learning information in switching nonlinear GRNs.
ContributorsVélez-Cruz, Nayely (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Moraffah, Bahman (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2023
Description
The present outlook in power systems is rapidly changing due to the introduction of (1) new active devices into the grid, such as photovoltaic (PV) panels, wind generators, and energy storage devices, and (2) new data from sensing and control devices. While this abundant data improves situational awareness and enhances control schemes, it can make the power grid more vulnerable than ever to cyber-attacks with dire consequences. Cyberattack withdraws much attention due to its potential impact, its financial losses, and its implications for national security. To understand the risks, this work looks into the operation of the electric grids, e.g., how to solve power flow equations. Specifically, this work investigates the good and the bad parts of existing methods and proposes to have a stochastic solution for power flow analysis for robustness. The finding is that no matter how the solution method is improved, system information is crucial to securely analyzing the grid. This gives utilities a false sense of security by hiding such information. For example, in a false data injection attack (FDIA), an attacker must know system information and measurements. If system information is hidden, the grid seems impossible to attack successfully, e.g., passing the Chi-square test based on system information. This dissertation shows that a carefully designed system can not only attack successfully but also with a strong performance guarantee.
ContributorsCostilla-Enriquez, Napoleon (Author) / Weng, Yang YG (Thesis advisor) / Arizona State University (Publisher)
Created2023
Description
In this dissertation, I investigate the electronic properties of two important silicon(Si)-based heterojunctions 1) hydrogenated amorphous silicon/crystalline silicon (a-Si:H/c-Si) which has already been commercialized in Heterojunction with Intrinsic Thin-layer (HIT) cells and 2) gallium phosphide/silicon (GaP/Si) which has been suggested to be a good candidate for replacing a-Si:H/c-Si in HIT cells in order to boost the HIT cell’s efficiency.
In the first part, the defect states of amorphous silicon (a-Si) and a-Si:H material are studied using density functional theory (DFT). I first employ simulated annealing using molecular dynamics (MD) to create stable configurations of a-Si:H, and then analyze the atomic and electronic structure to investigate which structural defects interact with H, and how the electronic structure changes with H addition. I find that H atoms decrease the density of mid-gap states and increase the band gap of a-Si by binding to Si atoms with strained bonds. My results also indicate that Si atoms with strained bonds creates high-localized orbitals in the mobility gap of a-Si, and the binding of H atoms to them can dramatically decrease their degree of localization.
In the second part, I explore the effect of the H binding configuration on the electronic properties of a-Si:H/c-Si heterostructure using density functional theory studies of models of the interface between a-Si:H and c-Si. The electronic properties from DFT show that depending on the energy difference between configurations, the electronic properties are sensitive to the H binding configurations.
In the last part, I examine the electronic structure of GaP/Si(001) heterojunctions and the effect of hydrogen H passivation at the interface in comparison to interface mixing, through DFT calculations. My calculations show that due to the heterovalent mismatch nature of the GaP/Si interface, there is a high density of localized states at the abrupt GaP/Si interface due to the excess charge associated with heterovalent bonding, as reported elsewhere. I find that the addition of H leads to additional bonding at the interface which mitigates the charge imbalance, and greatly reduces the density of localized states, leading to a nearly ideal heterojunction.
In the first part, the defect states of amorphous silicon (a-Si) and a-Si:H material are studied using density functional theory (DFT). I first employ simulated annealing using molecular dynamics (MD) to create stable configurations of a-Si:H, and then analyze the atomic and electronic structure to investigate which structural defects interact with H, and how the electronic structure changes with H addition. I find that H atoms decrease the density of mid-gap states and increase the band gap of a-Si by binding to Si atoms with strained bonds. My results also indicate that Si atoms with strained bonds creates high-localized orbitals in the mobility gap of a-Si, and the binding of H atoms to them can dramatically decrease their degree of localization.
In the second part, I explore the effect of the H binding configuration on the electronic properties of a-Si:H/c-Si heterostructure using density functional theory studies of models of the interface between a-Si:H and c-Si. The electronic properties from DFT show that depending on the energy difference between configurations, the electronic properties are sensitive to the H binding configurations.
In the last part, I examine the electronic structure of GaP/Si(001) heterojunctions and the effect of hydrogen H passivation at the interface in comparison to interface mixing, through DFT calculations. My calculations show that due to the heterovalent mismatch nature of the GaP/Si interface, there is a high density of localized states at the abrupt GaP/Si interface due to the excess charge associated with heterovalent bonding, as reported elsewhere. I find that the addition of H leads to additional bonding at the interface which mitigates the charge imbalance, and greatly reduces the density of localized states, leading to a nearly ideal heterojunction.
ContributorsVatan Meidanshahi, Reza (Author) / Goodnick, Stephen Marshall (Thesis advisor) / Vasileska, Dragica (Committee member) / Bowden, Stuart (Committee member) / Honsberg, Christiana (Committee member) / Arizona State University (Publisher)
Created2019
Description
Over the past few years, research into the use of doped diamond in electronics has seen an exponential growth. In the course of finding ways to reduce the contact resistivity, nanocarbon materials have been an interesting focus. In this work, the transfer length method (TLM) was used to investigate Ohmic contact properties using the tri-layer stack Ti/Pt/Au on nitrogen-doped n-type conducting nano-carbon (nanoC) layers grown on (100) diamond substrates. The nanocarbon material was characterized using Secondary Ion Mass Spectrometry (SIMS), Scanning electron Microscopy (SEM) X-ray diffraction (XRD), Raman scattering and Hall effect measurements to probe the materials characteristics. Room temperature electrical measurements were taken, and samples were annealed to observe changes in electrical conductivity. Low specific contact resistivity values of 8 x 10^-5 Ωcm^2 were achieved, which was almost two orders of magnitude lower than previously reported values. The results were attributed to the increased nitrogen incorporation, and the presence of electrically active defects which leads to an increase in conduction in the nanocarbon. Further a study of light phosphorus doped layers using similar methods with Ti/Pt/Au contacts again yielded a low contact resistivity of about 9.88 x 10^-2 Ωcm^2 which is an interesting prospect among lightly doped diamond films for applications in devices such as transistors. In addition, for the first time, hafnium was substituted for Ti in the contact stack (Hf/Pt/Au) and studied on nitrogen doped nanocarbon films, which resulted in low contact resistivity values on the order of 10^-2 Ωcm^2. The implications of the results were discussed, and recommendations for improving the experimental process was outlined. Lastly, a method for the selective area growth of nanocarbon was developed and studied and the results provided an insight into how different characterizations can be used to confirm the presence of the nanocrystalline diamond material, the limitations due to the film thickness was explored and ideas for future work was proposed.
ContributorsAmonoo, Evangeline Abena (Author) / Thornton, Trevor (Thesis advisor) / Alford, Terry L (Thesis advisor) / Anwar, Shahriar (Committee member) / Theodore, David (Committee member) / Arizona State University (Publisher)
Created2023
Description
Scaling of the Metal-Oxide-Semiconductor Field Effect Transistor (MOSFET) towards shorter channel lengths, has lead to an increasing importance of quantum effects on the device performance. Until now, a semi-classical model based on Monte Carlo method for instance, has been sufficient to address these issues in silicon, and arrive at a reasonably good fit to experimental mobility data. But as the semiconductor world moves towards 10nm technology, many of the basic assumptions in this method, namely the very fundamental Fermi’s golden rule come into question. The derivation of the Fermi’s golden rule assumes that the scattering is infrequent (therefore the long time limit) and the collision duration time is zero. This thesis overcomes some of the limitations of the above approach by successfully developing a quantum mechanical simulator that can model the low-field inversion layer mobility in silicon MOS capacitors and other inversion layers as well. It solves for the scattering induced collisional broadening of the states by accounting for the various scattering mechanisms present in silicon through the non-equilibrium based near-equilibrium Green’s Functions approach, which shall be referred to as near-equilibrium Green’s Function (nEGF) in this work. It adopts a two-loop approach, where the outer loop solves for the self-consistency between the potential and the subband sheet charge density by solving the Poisson and the Schrödinger equations self-consistently. The inner loop solves for the nEGF (renormalization of the spectrum and the broadening of the states), self-consistently using the self-consistent Born approximation, which is then used to compute the mobility using the Green-Kubo Formalism.
ContributorsJayaram Thulasingam, Gokula Kannan (Author) / Vasileska, Dragica (Thesis advisor) / Ferry, David (Committee member) / Goodnick, Stephen (Committee member) / Allee, David (Committee member) / Arizona State University (Publisher)
Created2017
Description
An efficient thermal solver is available in the CMC that allows modeling self-heating in the electrical simulations, which treats phonons as flux and solves the energy balance equation to quantify thermal effects. Using this solver, thermal simulations were performed on GaN-HEMTs in order to test effect of gate architectures on the DC and RF performance of the device. A Π- gate geometry is found to suppress 19.75% more hot electrons corresponding to a DC power of 2.493 W/mm for Vgs = -0.6V (max transconductance) with respect to the initial T-gate. For the DC performance, the output current, Ids is nearly same for each device configuration over the entire bias range. For the RF performance, the current gain was evaluated over a frequency range 20 GHz to 120 GHz in each device for both thermal (including self-heating) and isothermal (without self-heating). The evaluated cutoff frequency is around 7% lower for the thermal case than the isothermal case. The simulated cutoff frequency closely follows the experimental cutoff frequency. The work was extended to the study of ultra-wide bandgap material (Diamond), where isotope effect causes major deterioration in thermal conductivity. In this case, bulk phonons are modeled as semiclassical particles solving the nonlinear Peierls - Boltzmann transport equation with a stochastic approach. Simulations were performed for 0.001% (ultra-pure), 0.1% and 1.07% isotope concentration (13C) of diamond, showing good agreement with the experimental values. Further investigation was performed on the effect of isotope on the dynamics of individual phonon branches, thermal conductivity and the mean free path, to identify the dominant phonon branch. Acoustic phonons are found to be the principal contributors to thermal conductivity across all isotope concentrations with transverse acoustic (TA2) branch is the dominant branch with a contribution of 40% at room temperature and 37% at 500K. Mean free path computations show the lower bound of device dimensions in order to obtain maximum thermal conductivity. At 300K, the lowest mean free path (which is attributed to Longitudinal Optical phonon) reduces from 24nm to 8 nm for isotope concentration of 0.001% and 1.07% respectively. Similarly, the maximum mean free path (which is attributed to Longitudinal Acoustic phonon) reduces from 4 µm to 3.1 µm, respectively, for the same isotope concentrations. Furthermore, PETSc (Portable, Extensible Toolkit for Scientific Computation) developed by Argonne National Lab, was included in the existing Cellular Monte Carlo device simulator as a Poisson solver to further extend the capability of the simulator. The validity of the solver was tested performing 2D and 3D simulations and the results were compared with the well-established multigrid Poisson solver.
ContributorsAcharjee, Joy (Author) / Saraniti, Marco (Thesis advisor) / Goodnick, Stephen (Committee member) / Thornton, Trevor (Committee member) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2024
Description
Recent advancements in communication standards, such as 5G demand transmitter hardware to support high data rates with high energy efficiency. With the revolution of communication standards, modulation schemes have become more complex and require high peak-to-average (PAPR) signals. In wireless transceiver hardware, the power amplifier (PA) consumes most of the transceiver’s DC power and is typically the bottleneck for transmitter linearity. Therefore, the transmitter’s performance directly depends on the PA. To support high PAPR signals, the PA must operate efficiently at its saturated and backoff output power. Maintaining high efficiency at both peak and backoff output power is challenging. One effective technique for addressing this problem is load modulation. Some of the prominent load-modulated PA architectures are outphasing PAs, load-modulated balanced amplifiers (LMBA), envelope elimination and restoration (EER), envelope tracking (ET), Doherty power amplifiers (DPA), and polar transmitters. Amongst them, the DPA is the most popular for infrastructure applications due to its simpler architecture compared to other techniques and linearizability with digital pre-distortion (DPD). Another crucial characteristic of progressing communication standards is wide signal bandwidths. High-efficiency power amplifiers like class J/F/F-1 and load-modulated PAs like the DPA exhibit narrowband performance because the amplifiers require precise output impedance terminations. Therefore, it is equally essential to develop adaptable PA solutions to process radio frequency (RF) signals with wide bandwidths. To support modern and future cellular infrastructure, RF PAs need to be innovated to increase the backoff power efficiency by two times or more and support ten times or more wider bandwidths than current state-of-the-art PAs. This work presents five RF PA analyses and implementations to support future wireless communications transmitter hardware. Chapter 2 presents an optimized output-matching network analysis and design to achieve extended output power backoff of the DPA. Chapters 3 and 4 unveil two bandwidth enhancement techniques for the DPA while maintaining extended output power backoff. Chapter 5 exhibits a dual-band hybrid mode PA design targeted for wideband applications. Chapter 6 presents a built-in self-test circuit integrated into a PA for output impedance monitoring. This can alleviate the PA performance degradation due to the variation in the PA's output load over frequency, process, and aging. All RF PAs in this dissertation are implemented using Gallium Nitride (GaN)-based high electron mobility transistors (HEMT), and the realized designs validate the proposed PAs' theories/architectures.
ContributorsRoychowdhury, Debatrayee (Author) / Kitchen, Jennifer (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Ozev, Sule (Committee member) / Aberle, James (Committee member) / Arizona State University (Publisher)
Created2024