Matching Items (1,183)
Filtering by

Clear all filters

161459-Thumbnail Image.png
Description
This paper introduces an application space of Power over Ethernet to Universal Serial Bus (USB) Power Delivery, and develops 3 different flyback approaches to a 45 Watt solution in the space. The designs of Fixed Frequency Flyback, Quasi-Resonant Flyback, and Active Clamp Flyback are developed for the application with 37

This paper introduces an application space of Power over Ethernet to Universal Serial Bus (USB) Power Delivery, and develops 3 different flyback approaches to a 45 Watt solution in the space. The designs of Fixed Frequency Flyback, Quasi-Resonant Flyback, and Active Clamp Flyback are developed for the application with 37 Volts (V) to 57 V Direct Current (DC) input voltage and 5 V, 9 V, 15 V, and 20 V output, and results are examined for the given specifications. Implementation based concerns are addressed for each topology during the design process. The systems are proven and tested for efficiency, thermals, and output voltage ripple across the operation range. The topologies are then compared for a cost and benefit analysis and their highlights are identified to showcase each systems prowess.
ContributorsNasir, Anthony Michael (Author) / Ayyanar, Raja (Thesis advisor) / Lei, Qin (Committee member) / Hari, Ajay (Committee member) / Arizona State University (Publisher)
Created2021
161561-Thumbnail Image.png
Description
A distributed wireless sensor network (WSN) is a network of a large number of lowcost,multi-functional sensors with power, bandwidth, and memory constraints, operating in remote environments with sensing and communication capabilities. WSNs are a source for a large amount of data and due to the inherent communication and resource constraints, developing a distributed

A distributed wireless sensor network (WSN) is a network of a large number of lowcost,multi-functional sensors with power, bandwidth, and memory constraints, operating in remote environments with sensing and communication capabilities. WSNs are a source for a large amount of data and due to the inherent communication and resource constraints, developing a distributed algorithms to perform statistical parameter estimation and data analysis is necessary. In this work, consensus based distributed algorithms are developed for distributed estimation and processing over WSNs. Firstly, a distributed spectral clustering algorithm to group the sensors based on the location attributes is developed. Next, a distributed max consensus algorithm robust to additive noise in the network is designed. Furthermore, distributed spectral radius estimation algorithms for analog, as well as, digital communication models are developed. The proposed algorithms work for any connected graph topologies. Theoretical bounds are derived and simulation results supporting the theory are also presented.
ContributorsMuniraju, Gowtham (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Berisha, Visar (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2021
161574-Thumbnail Image.png
Description
As the field of machine learning increasingly provides real value to power system operations, the availability of rich measurement datasets has become crucial for the development of new applications and technologies. This dissertation focuses on the use of time-series load data for the design of novel data-driven algorithms. Loads are

As the field of machine learning increasingly provides real value to power system operations, the availability of rich measurement datasets has become crucial for the development of new applications and technologies. This dissertation focuses on the use of time-series load data for the design of novel data-driven algorithms. Loads are one of the main factors driving the behavior of a power system and they depend on external phenomena which are not captured by traditional simulation tools. Thus, accurate models that capture the fundamental characteristics of time-series load dataare necessary. In the first part of this dissertation, an example of successful application of machine learning algorithms that leverage load data is presented. Prior work has shown that power systems energy management systems are vulnerable to false data injection attacks against state estimation. Here, a data-driven approach for the detection and localization of such attacks is proposed. The detector uses historical data to learn the normal behavior of the loads in a system and subsequently identify if any of the real-time observed measurements are being manipulated by an attacker. The second part of this work focuses on the design of generative models for time-series load data. Two separate techniques are used to learn load behaviors from real datasets and exploiting them to generate realistic synthetic data. The first approach is based on principal component analysis (PCA), which is used to extract common temporal patterns from real data. The second method leverages conditional generative adversarial networks (cGANs) and it overcomes the limitations of the PCA-based model while providing greater and more nuanced control on the generation of specific types of load profiles. Finally, these two classes of models are combined in a multi-resolution generative scheme which is capable of producing any amount of time-series load data at any sampling resolution, for lengths ranging from a few seconds to years.
ContributorsPinceti, Andrea (Author) / Sankar, Lalitha (Thesis advisor) / Kosut, Oliver (Committee member) / Pal, Anamitra (Committee member) / Weng, Yang (Committee member) / Arizona State University (Publisher)
Created2021
161588-Thumbnail Image.png
Description
Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional methods of steady-state security assessment in power systems involve contingency analysis based on AC or DC power flows. However, power flow based contingency analysis is not

Ensuring reliable operation of large power systems subjected to multiple outages is a challenging task because of the combinatorial nature of the problem. Traditional methods of steady-state security assessment in power systems involve contingency analysis based on AC or DC power flows. However, power flow based contingency analysis is not fast enough to evaluate all contingencies for real-time operations. Therefore, real-time contingency analysis (RTCA) only evaluates a subset of the contingencies (called the contingency list), and hence might miss critical contingencies that lead to cascading failures.This dissertation proposes a new graph-theoretic approach, called the feasibility test (FT) algorithm, for analyzing whether a contingency will create a saturated or over-loaded cut-set in a meshed power network; a cut-set denotes a set of lines which if tripped separates the network into two disjoint islands. A novel feature of the proposed approach is that it lowers the solution time significantly making the approach viable for an exhaustive real-time evaluation of the system. Detecting saturated cut-sets in the power system is important because they represent the vulnerable bottlenecks in the network. The robustness of the FT algorithm is demonstrated on a 17,000+ bus model of the Western Interconnection (WI). Following the detection of post-contingency cut-set saturation, a two-component methodology is proposed to enhance the reliability of large power systems during a series of outages. The first component combines the proposed FT algorithm with RTCA to create an integrated corrective action (iCA), whose goal is to secure the power system against post-contingency cut-set saturation as well as critical branch overloads. The second component only employs the results of the FT to create a relaxed corrective action (rCA) that quickly secures the system against saturated cut-sets. The first component is more comprehensive than the second, but the latter is computationally more efficient. The effectiveness of the two components is evaluated based upon the number of cascade triggering contingencies alleviated, and the computation time. Analysis of different case-studies on the IEEE 118-bus and 2000-bus synthetic Texas systems indicate that the proposed two-component methodology enhances the scope and speed of power system security assessment during multiple outages.
ContributorsSen Biswas, Reetam (Author) / Pal, Anamitra (Thesis advisor) / Vittal, Vijay (Committee member) / Undrill, John (Committee member) / Wu, Meng (Committee member) / Zhang, Yingchen (Committee member) / Arizona State University (Publisher)
Created2021
161503-Thumbnail Image.png
Description
Wide Bandgap (WBG) semiconductor materials are shaping day-to-daytechnology by introducing powerful and more energy responsible devices. These materials have opened the door for building basic semiconductor devices which are superior in terms of handling high voltages, power and temperature which is not possible using conventional silicon technology. As the research continues in the

Wide Bandgap (WBG) semiconductor materials are shaping day-to-daytechnology by introducing powerful and more energy responsible devices. These materials have opened the door for building basic semiconductor devices which are superior in terms of handling high voltages, power and temperature which is not possible using conventional silicon technology. As the research continues in the field of WBG based devices, there is a potential chance that the semiconductor industry can save billions of dollars deploying energy-efficient circuits in high power conversion electronics. Diamond, silicon carbide and gallium nitride are the top three contenders among which diamond can significantly outmatch others in a variety of properties. This thesis describes a methodology to develop the ‘Simulation Program with Integrated Circuit Emphasis’ (SPICE) model for diamond-based P-I-N diodes. The developed model can predict the AC and DC response of fabricated P-I-N diodes. P-I-N diodes are semiconductor devices commonly used to control RF and microwave signals. It has found a very unique place in the list of available semiconductor devices in modern electronics which interestingly shows resistance modulation property in high frequency domain while handling a high-power signal at the same time. The developed SPICE model for the diamond-based P-I-N diode in this project is then used to evaluate the performance of a solid-state passive limiter in shunt configuration which protects the sensitive instruments in ‘Radio Detection and Ranging’ (RADAR) systems
ContributorsJHA, VISHAL (Author) / Trevor, Trevor TT (Thesis advisor) / Barnaby, Hugh HB (Committee member) / Aberle, James JA (Committee member) / Arizona State University (Publisher)
Created2021
161415-Thumbnail Image.png
Description
The broad deployment of time-synchronized continuous point-on-wave (CPoW) modules will enable electric power utilities to gain unprecedented insight into the behavior of their power system assets, loads, and distributed renewable generation in real time. By increasing the available level of detail visible to operators, serious fault events such as wildfire-inducing

The broad deployment of time-synchronized continuous point-on-wave (CPoW) modules will enable electric power utilities to gain unprecedented insight into the behavior of their power system assets, loads, and distributed renewable generation in real time. By increasing the available level of detail visible to operators, serious fault events such as wildfire-inducing arc flashes, safety-jeopardizing transformer failures, and equipment-damaging power quality decline can be mitigated in a data-driven, systematic manner. In this research project, a time-synchronized micro-scale CPoW module was designed, constructed, and characterized. This inductively powered CPoW module, which operates wirelessly by using the current flowing through a typical distribution conductor as its power source and a wireless data link for communication, has been configured to measure instantaneous line current at high frequency (nominally 3,000 samples per second) with 12-bit resolution. The design process for this module is detailed in this study, including background research, individual block design and testing, printed circuit board (PCB) design, and final characterization of the system. To validate the performance of this module, tests of power requirements, measurement accuracy, battery life, susceptibility to electromagnetic interference, and fault detection performance were performed. The results indicate that the design under investigation will satisfy the technical and physical constraints required for bulk deployment in an actual distribution network after manufacturing optimizations. After the test results were summarized, the future research and development activities needed to finalize this design for commercial deployment were identified and discussed.
ContributorsPatterson, John (Author) / Pal, Anamitra (Thesis advisor) / Ogras, Umit (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2021
Description
Contaminated aerosols and micro droplets are easily generated by infected hosts through sneezing, coughing, speaking and breathing1-3 and harm humans’ health and the global economy. While most of the efforts are usually targeted towards protecting individuals from getting infected,4 eliminating transmissions from infection sources is also important to prevent disease

Contaminated aerosols and micro droplets are easily generated by infected hosts through sneezing, coughing, speaking and breathing1-3 and harm humans’ health and the global economy. While most of the efforts are usually targeted towards protecting individuals from getting infected,4 eliminating transmissions from infection sources is also important to prevent disease transmission. Supportive therapies for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) pneumonia such as oxygen supplementation, nebulizers and non-invasive mechanical ventilation all carry an increased risk for viral transmission via aerosol to healthcare workers.5-9 In this work, I study the efficacy of five methods for self-containing aerosols emitted from infected subjects undergoing nebulization therapies with a diverse spectrum on Non-Invasive Positive Pressure Ventilator (NIPPV) with oxygen delivery therapies. The work includes five study cases: Case I: Use of a Full-Face Mask with biofilter in bilevel positive airway pressure device (BiPAP) therapy, Case II: Use of surgical mask in High Flow Nasal Cannula (HFNC) therapy, Case III: Use of a modified silicone disposable mask in a HFNC therapy, Case IV: Use of a modified silicone disposable mask with a regular nebulizer and normal breathing, Case V: Use of a mitigation box with biofilter in a BiPAP. We demonstrate that while cases I, III and IV showed efficacies of 98-100%; cases II and V, which are the most commonly used, resulted with significantly lower efficacies of 10-24% to mitigate the dispersion of nebulization aerosols. Therefore, implementing cases I, III and IV in health care facilities may help battle the contaminations and infections via aerosol transmission during a pandemic.
ContributorsShyamala Pandian, Adithya (Author) / Forzani, Erica (Thesis advisor) / Patel, Bhavesh (Committee member) / Xian, Xiaojun (Committee member) / Arizona State University (Publisher)
Created2021
161882-Thumbnail Image.png
Description
Crystalline silicon covers more than 85% of the global photovoltaics industry and has sustained a nearly 30% year-over-year growth rate. Continued cost and capital expenditure (CAPEX) reductions are needed to sustain this growth. Using thin silicon wafers well below the current industry standard of 160 µm can reduce manufacturing cost,

Crystalline silicon covers more than 85% of the global photovoltaics industry and has sustained a nearly 30% year-over-year growth rate. Continued cost and capital expenditure (CAPEX) reductions are needed to sustain this growth. Using thin silicon wafers well below the current industry standard of 160 µm can reduce manufacturing cost, CAPEX, and levelized cost of electricity. Additionally, thinner wafers enable more flexible and lighter module designs, making them more compelling in market segments like building-integrated photovoltaics, portable power, aerospace, and automotive industries. Advanced architectures and superior surface passivation schemes are needed to enable the use of very thin silicon wafers. Silicon heterojunction (SHJ) and SHJ with interdigitated back contact solar cells have demonstrated open-circuit voltages surpassing 720 mV and the potential to surpass 25% conversion efficiency. These factors have led to an increasing interest in exploring SHJ solar cells on thin wafers. In this work, the passivation capability of the thin intrinsic hydrogenated amorphous silicon layer is improved by controlling the deposition temperature and the silane-to-hydrogen dilution ratio. An effective way to parametrize surface recombination is by using surface saturation current density and a very low surface saturation density is achieved on textured wafers for wafer thicknesses ranging between 40 and 180 µm which is an order of magnitude lesser compared to the prevalent industry standards. Implied open-circuit voltages over 760 mV were accomplished on SHJ structures deposited on n-type silicon wafers with thicknesses below 50 µm. An analytical model is also described for a better understanding of the variation of the recombination fractions for varying substrate thicknesses. The potential of using very thin wafers is also established by manufacturing SHJ solar cells, using industrially pertinent processing steps, on 40 µm thin standalone wafers while achieving maximum efficiency of 20.7%. It is also demonstrated that 40 µm thin SHJ solar cells can be manufactured using these processes on large areas. An analysis of the percentage contribution of current, voltage, and resistive losses are also characterized for the SHJ devices fabricated in this work for varying substrate thicknesses.
ContributorsBalaji, Pradeep (Author) / Bowden, Stuart (Thesis advisor) / Alford, Terry (Thesis advisor) / Goryll, Michael (Committee member) / Augusto, Andre (Committee member) / Arizona State University (Publisher)
Created2021
161788-Thumbnail Image.png
Description
Collision-free path planning is also a major challenge in managing unmanned aerial vehicles (UAVs) fleets, especially in uncertain environments. The design of UAV routing policies using multi-agent reinforcement learning has been considered, and propose a Multi-resolution, Multi-agent, Mean-field reinforcement learning algorithm, named 3M-RL, for flight planning, where multiple vehicles need

Collision-free path planning is also a major challenge in managing unmanned aerial vehicles (UAVs) fleets, especially in uncertain environments. The design of UAV routing policies using multi-agent reinforcement learning has been considered, and propose a Multi-resolution, Multi-agent, Mean-field reinforcement learning algorithm, named 3M-RL, for flight planning, where multiple vehicles need to avoid collisions with each other while moving towards their destinations. In this system, each UAV makes decisions based on local observations, and does not communicate with other UAVs. The algorithm trains a routing policy using an Actor-Critic neural network with multi-resolution observations, including detailed local information and aggregated global information based on mean-field. The algorithm tackles the curse-of-dimensionality problem in multi-agent reinforcement learning and provides a scalable solution. The proposed algorithm is tested in different complex scenarios in both 2D and 3D space and the simulation results show that 3M-RL result in good routing policies. Also as a compliment, dynamic data communications between UAVs and a control center has also been studied, where the control center needs to monitor the safety state of each UAV in the system in real time, where the transition of risk level is simply considered as a Markov process. Given limited communication bandwidth, it is impossible for the control center to communicate with all UAVs at the same time. A dynamic learning problem with limited communication bandwidth is also discussed in this paper where the objective is to minimize the total information entropy in real-time risk level tracking. The simulations also demonstrate that the algorithm outperforms policies such as a Round & Robin policy.
ContributorsWang, Weichang (Author) / Ying, Lei (Thesis advisor) / Liu, Yongming (Thesis advisor) / Zhang, Junshan (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2021
161802-Thumbnail Image.png
Description
Rapid increases in the installed amounts of Distributed Energy Resources are forcing a paradigm shift to guarantee stability, security, and economics of power distribution systems. This dissertation explores these challenges and proposes solutions to enable higher penetrations of grid-edge devices. The thesis shows that integrating Graph Signal Processing with State

Rapid increases in the installed amounts of Distributed Energy Resources are forcing a paradigm shift to guarantee stability, security, and economics of power distribution systems. This dissertation explores these challenges and proposes solutions to enable higher penetrations of grid-edge devices. The thesis shows that integrating Graph Signal Processing with State Estimation formulation allows accurate estimation of voltage phasors for radial feeders under low-observability conditions using traditional measurements. Furthermore, the Optimal Power Flow formulation presented in this work can reduce the solution time of a bus injection-based convex relaxation formulation, as shown through numerical results. The enhanced real-time knowledge of the system state is leveraged to develop new approaches to cyber-security of a transactive energy market by introducing a blockchain-based Electron Volt Exchange framework that includes a distributed protocol for pricing and scheduling prosumers' production/consumption while keeping constraints and bids private. The distributed algorithm prevents power theft and false data injection by comparing prosumers' reported power exchanges to models of expected power exchanges using measurements from grid sensors to estimate system state. Necessary hardware security is described and integrated into underlying grid-edge devices to verify the provenance of messages to and from these devices. These preventive measures for securing energy transactions are accompanied by additional mitigation measures to maintain voltage stability in inverter-dominated networks by expressing local control actions through Lyapunov analysis to mitigate cyber-attack and generation intermittency effects. The proposed formulation is applicable as long as the Volt-Var and Volt-Watt curves of the inverters can be represented as Lipschitz constants. Simulation results demonstrate how smart inverters can mitigate voltage oscillations throughout the distribution network. Approaches are rigorously explored and validated using a combination of real distribution networks and synthetic test cases. Finally, to overcome the scarcity of real data to test distribution systems algorithms a framework is introduced to generate synthetic distribution feeders mapped to real geospatial topologies using available OpenStreetMap data. The methods illustrate how to create synthetic feeders across the entire ZIP Code, with minimal input data for any location. These stackable scientific findings conclude with a brief discussion of physical deployment opportunities to accelerate grid modernization efforts.
ContributorsSaha, Shammya Shananda (Author) / Johnson, Nathan (Thesis advisor) / Scaglione, Anna (Thesis advisor) / Arnold, Daniel (Committee member) / Boscovic, Dragan (Committee member) / Arizona State University (Publisher)
Created2021