Matching Items (184)
Description
Renewable energy has been a very hot topic in recent years due to the traditional energy crisis. Incentives that encourage the renewables have been established all over the world. Ordinary homeowners are also seeking ways to exploit renewable energy. In this thesis, residential PV system, wind turbine system and a hybrid wind/solar system are all investigated. The solar energy received by the PV panels varies with many factors. The most essential one is the irradiance. As the PV panel been installed towards different orientations, the incident insolation received by the panel also will be different. The differing insolation corresponds to the different angles between the irradiance and the panel throughout the day. The result shows that for PV panels in the northern hemisphere, the ones facing south obtain the highest level insolation and thus generate the most electricity. However, with the two different electricity rate plans, flat rate plan and TOU (time of use) plan, the value of electricity that PV generates is different. For wind energy, the wind speed is the most significant variable to determine the generation of a wind turbine. Unlike solar energy, wind energy is much more regionally dependent. Wind resources vary between very close locations. As expected, the result shows that, larger wind speed leads to more electricity generation and thus shorter payback period. For the PV/wind hybrid system, two real cases are analyzed for Altamont and Midhill, CA. In this part, the impact of incentives, system cost and system size are considered. With a hybrid system, homeowners may choose different size combinations between PV and wind turbines. It turns out that for these two locations, the system with larger PV output always achieve a shorter payback period due to the lower cost. Even though, for a longer term, the system with a larger wind turbine in locations with excellent wind resources may lead to higher return on investment. Meanwhile, impacts of both wind and solar incentives (mainly utility rebates) are analyzed. At last, effects of the cost of both renewables are performed.
ContributorsAn, Wen (Author) / Holbert, Keith E. (Thesis advisor) / Karady, George G. (Committee member) / Tylavsky, Daniel (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this thesis, an issue is post at the beginning, that there is limited experience in connecting a battery analytical model with a battery circuit model. Then it describes the process of creating a new battery circuit model which is referred to as the kinetic battery model. During this process, a new general equation is derived. The original equation in the kinetic battery model is only valid at a constant current rate, while the new equation can be used for not only constant current but also linear or nonlinear current. Following the new equation, a circuit representation is built based on the kinetic battery model. Then, by matching the two sets of differential equations of the two models together, the ability to connect the analytical model with the battery circuit model is found. To verify the new battery circuit model is built correctly, the new circuit model is implemented into PSpice simulation software to test the charging performance with constant current, and Matlab/Simulink is also employed to simulate a realistic battery charging process with two-stage charging method. The results have shown the new circuit model is available to be used in realistic scenarios. And because the kinetic battery model can describe different types of rechargeable batteries, the new circuit model is also capable to be used for various battery types.
ContributorsKong, Dexinghui (Author) / Holbert, Keith E. (Thesis advisor) / Karady, George G. (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2012
Description
Power generation in remote isolated places is a tough problem. Presently, a common source for remote generation is diesel. However, diesel generation is costly and environmental unfriendly. It is promising to replace the diesel generation with some clean and economical generation sources. The concept of renewable generation offers a solution to remote generation. This thesis focuses on evaluation of renewable generation penetration in the remote isolated grid. A small town named Coober Pedy in South Australia is set as an example. The first task is to build the stochastic models of solar irradiation and wind speed based on the local historical data. With the stochastic models, generation fluctuations and generation planning are further discussed. Fluctuation analysis gives an evaluation of storage unit size and costs. Generation planning aims at finding the relationships between penetration level and costs under constraint of energy sufficiency. The results of this study provide the best penetration level that makes the minimum energy costs. In the case of Coober Pedy, cases of wind and photovoltaic penetrations are studied. The additional renewable sources and suspended diesel generation change the electricity costs. Results show that in remote isolated grid, compared to diesel generation, renewable generation can lower the energy costs.
ContributorsZhu, Yujia (Author) / Holbert, Keith E. (Thesis advisor) / Karady, George G. (Committee member) / Tylavsky, Daniel J (Committee member) / Arizona State University (Publisher)
Created2012
Description
This undergraduate thesis explores the efficacy of developing a translator generator in the Prolog programming language using Lexical Functional Grammars. A bidirectional machine translator between English and Hungarian, developed as a proof-of-concept case study, is discussed and assessed. The benefits and drawbacks of this approach as generalized to Machine Translation systems are also discussed, along with possible areas of future work.
ContributorsLane, Ryan Andrew (Author) / Bansal, Ajay (Thesis director) / Bansal, Srividya (Committee member) / Barrett, The Honors College (Contributor)
Created2015-05
Description
Optical Instrument Transformers (OIT) have been developed as an alternative to traditional instrument transformers (IT). The question "Can optical instrument transformers substitute for the traditional transformers?" is the main motivation of this study. Finding the answer for this question and developing complete models are the contributions of this work. Dedicated test facilities are developed so that the steady state and transient performances of analog outputs of a magnetic current transformer (CT) and a magnetic voltage transformer (VT) are compared with that of an optical current transformer (OCT) and an optical voltage transformer (OVT) respectively. Frequency response characteristics of OIT outputs are obtained. Comparison results show that OITs have a specified accuracy of 0.3% in all cases. They are linear, and DC offset does not saturate the systems. The OIT output signal has a 40~60 μs time delay, but this is typically less than the equivalent phase difference permitted by the IEEE and IEC standards for protection applications. Analog outputs have significantly higher bandwidths (adjustable to 20 to 40 kHz) than the IT. The digital output signal bandwidth (2.4 kHz) of an OCT is significantly lower than the analog signal bandwidth (20 kHz) due to the sampling rates involved. The OIT analog outputs may have significant white noise of 6%, but the white noise does not affect accuracy or protection performance. Temperatures up to 50oC do not adversely affect the performance of the OITs. Three types of models are developed for analog outputs: analog, digital, and complete models. Well-known mathematical methods, such as network synthesis and Jones calculus methods are applied. The developed models are compared with experiment results and are verified with simulation programs. Results show less than 1.5% for OCT and 2% for OVT difference and that the developed models can be used for power system simulations and the method used for the development can be used to develop models for all other brands of optical systems. The communication and data transfer between the all-digital protection systems is investigated by developing a test facility for all digital protection systems. Test results show that different manufacturers' relays and transformers based on the IEC standard can serve the power system successfully.
ContributorsKucuksari, Sadik (Author) / Karady, George G. (Thesis advisor) / Heydt, Gerald T (Committee member) / Holbert, Keith E. (Committee member) / Ayyanar, Raja (Committee member) / Farmer, Richard (Committee member) / Arizona State University (Publisher)
Created2010
Description
The RADiation sensitive Field Effect Transistor (RADFET) has been conventionally used to measure radiation dose levels. These dose sensors are calibrated in such a way that a shift in threshold voltage, due to a build-up of oxide-trapped charge, can be used to estimate the radiation dose. In order to estimate the radiation dose level using RADFET, a wired readout circuit is necessary. Using the same principle of oxide-trapped charge build-up, but by monitoring the change in capacitance instead of threshold voltage, a wireless dose sensor can be developed. This RADiation sensitive CAPacitor (RADCAP) mounted on a resonant patch antenna can then become a wireless dose sensor. From the resonant frequency, the capacitance can be extracted which can be mapped back to estimate the radiation dose level. The capacitor acts as both radiation dose sensor and resonator element in the passive antenna loop. Since the MOS capacitor is used in passive state, characterizing various parameters that affect the radiation sensitivity is essential. Oxide processing technique, choice of insulator material, and thickness of the insulator, critically affect the dose response of the sensor. A thicker oxide improves the radiation sensitivity but reduces the dynamic range of dose levels for which the sensor can be used. The oxide processing scheme primarily determines the interface trap charge and oxide-trapped charge development; controlling this parameter is critical to building a better dose sensor.
ContributorsSrinivasan Gopalan, Madusudanan (Author) / Barnaby, Hugh (Thesis advisor) / Holbert, Keith E. (Committee member) / Yu, Hongyu (Committee member) / Arizona State University (Publisher)
Created2010
Description
In this paper, I explore practical applications of neural networks for automated skin lesion identification. The visual characteristics are of primary importance in the recognition of skin diseases, hence, the development of deep neural network models proven capable of classifying skin lesions can potentially change the face of modern medicine by extending the availability and lowering the cost of diagnostic care. Previous work has demonstrated the effectiveness of convolutional neural networks in image classification in general, with even higher accuracy achievable by data augmentation techniques, such as cropping, rotating, and flipping input images, along with more advanced computationally intensive approaches. In this research, I provide an overview of Convolutional Neural Networks (CNN) and CNN implementation with TensorFlow and Keras API in context of image recognition and classification. I also experiment with custom convolutional neural network model architecture trained using HAM10000 dataset. The dataset used for the case study is obtained from Harvard Dataverse and is maintained by Medical University of Vienna. The HAM10000 dataset is a large collection of multi-source dermatoscopic images of common pigmented skin lesions and is available for academic research under Creative Commons Attribution-Noncommercial 4.0 International Public License. With over ten thousand dermatoscopic images of seven classes of benign and malignant skin lesions, the dataset is substantial for academic machine learning purposes for multiclass image classification. I discuss the successes and shortcomings of the model in respect to its application to the dataset.
ContributorsKaraliova, Natallia (Author) / Bansal, Ajay (Thesis director) / Gonzalez-Sanchez, Javier (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Currently, students at Arizona State University are restricted to cards when using their college's local currency. This currency, Maroon and Gold dollars (M&G), is a primary source of meal plans for many students. When relying on card readers, students risk security and convenience. The security is risked due to the constant student id number on each card. A student's identification number never changes and is located on each card. If the student loses their card, their account information is permanently compromised. Convenience is an issue because, currently, students must make a purchase in order to see their current account balance. Another major issue is that businesses must purchase external hardware in order to use the M&G System. An online or mobile system would eliminate the need for a physical card and allow businesses to function without external card readers. Such a system would have access to financial information of businesses and students at ASU. Thus, the system require severe scrutiny by a well-trusted team of professionals before being implemented. My objective was to help bring such a system to life. To do this, I decided to make a mobile application prototype to serve as a baseline and to demonstrate the features of such a system. As a baseline, it needed to have a realistic, professional appearance, with the ability to accurately demonstrate feature functionality. Before developing the app, I set out to determine the User Interactions and User Experience designs (UI/UX) by conducting a series of informal interviews with local students and businesses. After the designs were finalized, I started implementation of the actual application in Android Studio. This creative project consists of a mobile application, a contained database, a GUI (Graphics User Interface) prototype, and a technical document.
ContributorsReigel, Justin Bryce (Author) / Bansal, Ajay (Thesis director) / Lindquist, Timothy (Committee member) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
37,461 automobile accident fatalities occured in the United States in 2016 ("Quick Facts 2016", 2017). Improving the safety of roads has traditionally been approached by governmental agencies including the National Highway Traffic Safety Administration and State Departments of Transporation. In past literature, automobile crash data is analyzed using time-series prediction technicques to identify road segments and/or intersections likely to experience future crashes (Lord & Mannering, 2010). After dangerous zones have been identified road modifications can be implemented improving public safety. This project introduces a historical safety metric for evaluating the relative danger of roads in a road network. The historical safety metric can be used to update routing choices of individual drivers improving public safety by avoiding historically more dangerous routes. The metric is constructed using crash frequency, severity, location and traffic information. An analysis of publically-available crash and traffic data in Allgeheny County, Pennsylvania is used to generate the historical safety metric for a specific road network. Methods for evaluating routes based on the presented historical safety metric are included using the Mann Whitney U Test to evaluate the significance of routing decisions. The evaluation method presented requires routes have at least 20 crashes to be compared with significance testing. The safety of the road network is visualized using a heatmap to present distribution of the metric throughout Allgeheny County.
ContributorsGupta, Ariel Meron (Author) / Bansal, Ajay (Thesis director) / Sodemann, Angela (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05