Matching Items (1,184)
Filtering by
- All Subjects: Electrical Engineering
- Status: Published
Description
Unlike conventional solar cells, modern high efficiency passivated contacts solar cells like silicon heterojunction (SHJ) cells have excellent surface passivation and use high bulk lifetime wafers which increase the operating injection level of these devices. These solar cell architectures can benefit from having lower doped substrates, with undoped solar cells becoming an attractive option. There has been very limited literature on high bulk resistivity substrates (>>10 Ωcm). This thesis work provides a comprehensive assessment of the potential of high resistivity/undoped substrates for high performance and more reliable silicon solar cells by demonstrating the results from modeling as well as characterization of SHJ solar cells fabricated with high resistivity/undoped substrates under real-world illumination and temperature conditions that the cells/modules experience in the field. In this work, the results from the analytical model demonstrated the effects of various defects, variation in doping and temperature on the performance of silicon solar cells. Experimentally, SHJ cells with bulk resistivities in the range of 1 Ωcm to >15k Ωcm were fabricated, and cell efficiencies over 20% were measured at standard testing conditions (STC) across the entire range of bulk resistivities. The illumination response (0.1-1 sun) and temperature coefficients (25-90 °C) were shown to be independent of the bulk resistivity. No light induced degradation was observed in the n-type SHJ cells of all resistivity ranges whereas high resistivity p-type SHJ cells showed less degradation compared to that of commercial resistivity range (<10 Ωcm). Very high reverse breakdown voltages (over 1 kV) were demonstrated for SHJ cells fabricated with high resistivity wafers. Using simulation, the importance of having cells in the modules with breakdown voltage higher than the series string voltage for safe and reliable operation of the photovoltaic (PV) system was highlighted.
The ingot yield can be improved by moving towards high resistivity ranges to manufacture high efficiency reliable solar cells by utilizing the entire ingot and eliminating the need to adhere to narrow resistivity range. Thus, the novel findings from this work can have profound impact on ingot and module manufacturing resulting in significant cost savings as well as improvement in the system reliability.
ContributorsSrinivasa, Apoorva (Author) / Bowden, Stuart (Thesis advisor) / Honsberg, Christiana (Committee member) / King, Richard (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2021
Description
Recent years have seen machine learning makes growing presence in several areas inwireless communications, and specifically in large-scale Multiple-Input Multiple-Output
(MIMO) systems. This comes as a result of its ability to offer innovative solutions to some
of the most daunting problems that haunt current and future large-scale MIMO systems,
such as downlink channel-training and sensitivity to line-of-sight (LOS) blockages to name
two examples. Machine learning, in general, provides wireless systems with data-driven
capabilities, with which they could realize much needed agility for decision-making and
adaptability to their surroundings. Bearing the potential of machine learning in mind, this
dissertation takes a close look at what deep learning can bring to the table of large-scale
MIMO systems. It proposes three novel frameworks based on deep learning that tackle
challenges rooted in the need to acquire channel state information. Framework 1, namely
deterministic channel prediction, recognizes that some channels are easier to acquire than
others (e.g., uplink are easier to acquire than downlink), and, as such, it learns a function
that predicts some channels (target channels) from others (observed channels). Framework
2, namely statistical channel prediction, aims to do the same thing as Framework 1, but it
takes a more statistical approach; it learns a large-scale statistic for target channels (i.e.,
per-user channel covariance) from observed channels. Differently from frameworks 1 and
2, framework 3, namely vision-aided wireless communications, presents an unorthodox
perspective on dealing with large-scale MIMO challenges specific to high-frequency communications.
It relies on the fact that high-frequency communications are reliant on LOS
much like computer vision. Therefore, it recognizes that parallel and utilizes multimodal
deep learning to address LOS-related challenges, such as downlink beam training and LOSlink
blockages. All three frameworks are studied and discussed using datasets representing
various large-scale MIMO settings. Overall, they show promising results that cement the
value of machine learning, especially deep learning, to large-scale MIMO systems.
ContributorsAlrabeiah, Muhammad (Author) / Alkhateeb, Ahmed A (Thesis advisor) / Turaga, Pavan P (Committee member) / Dasarathy, Gautam G (Committee member) / Tepedelenlioglu, Cihan C (Committee member) / Arizona State University (Publisher)
Created2021
Description
This work focuses on qualifying the performance of an optoelectrical measurement system designed to analyze ribonucleic acid (RNA) within a micro sample. The system is capable of measuring light intensity converted to voltage versus time and is a fast, inexpensive, and portable method for rapid detection of biologics such as SARS-CoV-2 virus, or Covid-19 disease. The measurement system consists of a microfluidic chip and a point of care fluorescent reader.The intent of this research is to measure consistency and robustness of the fluorescent reader combined with the microfluidic chip. The consistency and the robustness of the fluorescent reader within the duty cycle of the system power and the measurement system were analyzed with Six Sigma methods. Control charts, analysis of variance (ANOVAs), and variance components calculations were implemented to characterize the reader system. Through the process of this analysis, baseline characteristics were measured and documented providing valuable data for the improved instrument design.
The existing microfluidic chip is a prototype that works in combination with the reader based on fluorescent detection. Baseline studies were required to define any issues related to microfluidic autofluorescence. Multiple designs were tested to measure reduction in autofluorescence in the microfluidics. It was found that certain designs performed better than others. One approach for improvement in the microfluidic chip may be achieved by characterizing and source controlling materials, optimizing layers, mask apertures, and mask orientations to determine reliability in the measurable output through the fluorescent reader. Since the reader and the microfluidic are designed to work together, any future studies should explore testing where the two components are considered a coupled system.
ContributorsShabtai, Bat-El (Author) / Blain Christen, Jennifer (Thesis advisor) / Abbas, James (Thesis advisor) / Maass, Eric (Committee member) / Beeman, Scott (Committee member) / Arizona State University (Publisher)
Created2021
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
The advent of silicon, germanium, narrow-gap III-V materials, and later the wide bandgap (WBG) semiconductors, and their subsequent revolution and enrichment of daily life begs the question: what is the next generation of semiconductor electronics poised to look like? Ultrawide bandgap (UWBG) semiconductors are the class of semiconducting materials that possess an electronic bandgap (EG) greater than that of gallium nitride (GaN), which is 3.4 eV. They currently consist of beta-phase gallium oxide (β-Ga2O3 ; EG = 4.6–4.9 eV), diamond (EG = 5.5 eV), aluminum nitride (AlN; EG =6.2 eV), cubic boron nitride (BN; EG = 6.4 eV), and other materials hitherto undiscovered. Such a strong emphasis is placed on the semiconductor bandgap because so many relevant electronic performance properties scale positively with the bandgap. Where power electronics is concerned, the Baliga's Figure of Merit (BFOM) quantifies how much voltage a device can block in the off state and how high its conductivity is in the on state. The BFOM has a sixth-order dependence on the bandgap. The UWBG class of semiconductors also possess the potential for higher switching efficiencies and power densities and better suitability for deep-UV and RF optoelectronics. Many UWBG materials have very tight atomic lattices and high displacement energies, which makes them suitable for extreme applications such as radiation-harsh environments commonly found in military, industrial, and outer space applications. In addition, the UWBG materials also show promise for applications in quantum information sciences. For all the inherent promise and burgeoning research efforts, key breakthroughs in UWBG research have only occurred as recently as within the last two to three decades, making them extremely immature in comparison with the well-known WBG materials and others before them. In particular, AlN suffers from a lack of wide availability of low-cost, highquality substrates, a stark contrast to β-Ga2O3, which is now readily commercially available. In order to realize more efficient and varied devices on the relatively nascent UWBG materials platform, a deeper understanding of the various devices and physics is necessary. The following thesis focuses on the UWBG materials AlN and β-Ga2O3, overlooking radiation studies, a novel device heterojunction, and electronic defect study.
ContributorsMontes, Jossue (Author) / Zhao, Yuji (Thesis advisor) / Vasileska, Dragica (Committee member) / Goodnick, Stephen (Committee member) / Sanchez Esqueda, Ivan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Advancements in technologies like the Internet of thing causes an increase in the presence of wireless transceivers. A cooperative communication between these transceivers opens a doorway for multiple novel applications. A mobile distributed transceiver architecture is a much more dynamic environment dictating the necessity of faster synchronization among the transceivers. A possibility of simultaneous synchronization in parallel with the communication will theoretically ensure a high-speed synchronization without affecting the data rate. One such system has been implemented using a Costas loop and an extension of such synchronization technique to the full-duplex model has also been addressed. The rise in spectral demand is hard to meet with the regular Time duplex and frequency duplex communication systems. A full-duplex system is theoretically expected to double the spectral efficiency. However it comes with tremendous challenges, This thesis works on one of those challenges in implementing full-duplex synchronization. A coherent full-duplex model is designed to overcome the issue of transmitter leakage modeled as injection pulling, A known solution for this effect has been used to resolve the issue and complete the coherent full-duplex model. This establishes the simultaneous synchronization and communication system.
ContributorsDhulipala, Sailesh (Author) / Zeinolabedinzadeh, Saeed (Thesis advisor) / Trichopoulos, Georgios C. (Committee member) / Allee, David (Committee member) / Arizona State University (Publisher)
Created2021
Description
This creative project is a part of the work being done as a Senior Design Project in which an autonomous solar charge controller is being developed. The goal of this project is to design and build a prototype of an autonomous solar charge controller that can work independently of the power grid. This solar charge controller is being built for a community in Monument Valley, Arizona who live off grid. The controller is designed to step down power supplied by an array of solar panels to charge a 48V battery and supply power to an inverter. The charge controller can implement MPPT (Maximum Power Point Tracking) to charge the battery and power the inverter, it also is capable of disconnecting from the battery when the battery is fully charged and reconnecting when it detects that the battery has discharged. The charge controller can also switch from supplying power to the inverter from the panel to supplying power from the battery at low sun or night. These capabilities are not found in solar charge controllers that are on the market. This project aims to achieve all these capabilities and provide a solution for the problems being faced by the current solar charge controller
ContributorsSingh, Khushi (Author) / Goryll, Michael (Thesis director) / Kitchen, Jennifer (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2021-12
Description
In this dissertation, I described my research on the growth and characterization of various nanostructures, such as nanowires, nanobelts and nanosheets, of different semiconductors in a Chemical Vapor Deposition (CVD) system.
In the first part of my research, I selected chalcogenides (such as CdS and CdSe) for a comprehensive study in growing two-segment axial nanowires and radial nanobelts/sheets using the ternary CdSxSe1-x alloys. I demonstrated simultaneous red (from CdSe-rich) and green (from CdS-rich) light emission from a single monolithic heterostructure with a maximum wavelength separation of 160 nm. I also demonstrated the first simultaneous two-color lasing from a single nanosheet heterostructure with a wavelength separation of 91 nm under sufficiently strong pumping power.
In the second part, I considered several combinations of source materials with different growth methods in order to extend the spectral coverage of previously demonstrated structures towards shorter wavelengths to achieve full-color emissions. I achieved this with the growth of multisegment heterostructure nanosheets (MSHNs), using ZnS and CdSe chalcogenides, via our novel growth method. By utilizing this method, I demonstrated the first growth of ZnCdSSe MSHNs with an overall lattice mismatch of 6.6%, emitting red, green and blue light simultaneously, in a single furnace run using a simple CVD system. The key to this growth method is the dual ion exchange process which converts nanosheets rich in CdSe to nanosheets rich in ZnS, demonstrated for the first time in this work. Tri-chromatic white light emission with different correlated color temperature values was achieved under different growth conditions. We demonstrated multicolor (191 nm total wavelength separation) laser from a single monolithic semiconductor nanostructure for the first time. Due to the difficulties associated with growing semiconductor materials of differing composition on a given substrate using traditional planar epitaxial technology, our nanostructures and growth method are very promising for various device applications, including but not limited to: illumination, multicolor displays, photodetectors, spectrometers and monolithic multicolor lasers.
In the first part of my research, I selected chalcogenides (such as CdS and CdSe) for a comprehensive study in growing two-segment axial nanowires and radial nanobelts/sheets using the ternary CdSxSe1-x alloys. I demonstrated simultaneous red (from CdSe-rich) and green (from CdS-rich) light emission from a single monolithic heterostructure with a maximum wavelength separation of 160 nm. I also demonstrated the first simultaneous two-color lasing from a single nanosheet heterostructure with a wavelength separation of 91 nm under sufficiently strong pumping power.
In the second part, I considered several combinations of source materials with different growth methods in order to extend the spectral coverage of previously demonstrated structures towards shorter wavelengths to achieve full-color emissions. I achieved this with the growth of multisegment heterostructure nanosheets (MSHNs), using ZnS and CdSe chalcogenides, via our novel growth method. By utilizing this method, I demonstrated the first growth of ZnCdSSe MSHNs with an overall lattice mismatch of 6.6%, emitting red, green and blue light simultaneously, in a single furnace run using a simple CVD system. The key to this growth method is the dual ion exchange process which converts nanosheets rich in CdSe to nanosheets rich in ZnS, demonstrated for the first time in this work. Tri-chromatic white light emission with different correlated color temperature values was achieved under different growth conditions. We demonstrated multicolor (191 nm total wavelength separation) laser from a single monolithic semiconductor nanostructure for the first time. Due to the difficulties associated with growing semiconductor materials of differing composition on a given substrate using traditional planar epitaxial technology, our nanostructures and growth method are very promising for various device applications, including but not limited to: illumination, multicolor displays, photodetectors, spectrometers and monolithic multicolor lasers.
ContributorsTurkdogan, Sunay (Author) / Ning, Cun Zheng (Thesis advisor) / Palais, Joseph C. (Committee member) / Yu, Hongbin (Committee member) / Mardinly, A. John (Committee member) / Arizona State University (Publisher)
Created2015
Description
This research primarily deals with the design and validation of the protection system for a large scale meshed distribution system. The large scale system simulation (LSSS) is a system level PSCAD model which is used to validate component models for different time-scale platforms, to provide a virtual testing platform for the Future Renewable Electric Energy Delivery and Management (FREEDM) system. It is also used to validate the cases of power system protection, renewable energy integration and storage, and load profiles. The protection of the FREEDM system against any abnormal condition is one of the important tasks. The addition of distributed generation and power electronic based solid state transformer adds to the complexity of the protection. The FREEDM loop system has a fault current limiter and in addition, the Solid State Transformer (SST) limits the fault current at 2.0 per unit. Former students at ASU have developed the protection scheme using fiber-optic cable. However, during the NSF-FREEDM site visit, the National Science Foundation (NSF) team regarded the system incompatible for the long distances. Hence, a new protection scheme with a wireless scheme is presented in this thesis. The use of wireless communication is extended to protect the large scale meshed distributed generation from any fault. The trip signal generated by the pilot protection system is used to trigger the FID (fault isolation device) which is an electronic circuit breaker operation (switched off/opening the FIDs). The trip signal must be received and accepted by the SST, and it must block the SST operation immediately. A comprehensive protection system for the large scale meshed distribution system has been developed in PSCAD with the ability to quickly detect the faults. The validation of the protection system is performed by building a hardware model using commercial relays at the ASU power laboratory.
ContributorsSharma, Nitish (Author) / Karady, George G. (Thesis advisor) / Holbert, Keith E. (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2015
Description
Gas turbine engine for aircraft propulsion represents one of the most physics-complex and safety-critical systems in the world. Its failure diagnostic is challenging due to the complexity of the model system, difficulty involved in practical testing and the infeasibility of creating homogeneous diagnostic performance evaluation criteria for the diverse engine makes.
NASA has designed and publicized a standard benchmark problem for propulsion engine gas path diagnostic that enables comparisons among different engine diagnostic approaches. Some traditional model-based approaches and novel purely data-driven approaches such as machine learning, have been applied to this problem.
This study focuses on a different machine learning approach to the diagnostic problem. Some most common machine learning techniques, such as support vector machine, multi-layer perceptron, and self-organizing map are used to help gain insight into the different engine failure modes from the perspective of big data. They are organically integrated to achieve good performance based on a good understanding of the complex dataset.
The study presents a new hierarchical machine learning structure to enhance classification accuracy in NASA's engine diagnostic benchmark problem. The designed hierarchical structure produces an average diagnostic accuracy of 73.6%, which outperforms comparable studies that were most recently published.
NASA has designed and publicized a standard benchmark problem for propulsion engine gas path diagnostic that enables comparisons among different engine diagnostic approaches. Some traditional model-based approaches and novel purely data-driven approaches such as machine learning, have been applied to this problem.
This study focuses on a different machine learning approach to the diagnostic problem. Some most common machine learning techniques, such as support vector machine, multi-layer perceptron, and self-organizing map are used to help gain insight into the different engine failure modes from the perspective of big data. They are organically integrated to achieve good performance based on a good understanding of the complex dataset.
The study presents a new hierarchical machine learning structure to enhance classification accuracy in NASA's engine diagnostic benchmark problem. The designed hierarchical structure produces an average diagnostic accuracy of 73.6%, which outperforms comparable studies that were most recently published.
ContributorsWu, Qiyu (Author) / Si, Jennie (Thesis advisor) / Wu, Teresa (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2015