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- Genre: Masters Thesis
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- Creators: Arizona State University
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Description
Dynamic loading is the term used for one way of optimally loading a transformer. Dynamic loading means the utility takes into account the thermal time constant of the transformer along with the cooling mode transitions, loading profile and ambient temperature when determining the time-varying loading capability of a transformer. Knowing the maximum dynamic loading rating can increase utilization of the transformer while not reducing life-expectancy, delaying the replacement of the transformer. This document presents the progress on the transformer dynamic loading project sponsored by Salt River Project (SRP). A software application which performs dynamic loading for substation distribution transformers with appropriate transformer thermal models is developed in this project. Two kinds of thermal hottest-spot temperature (HST) and top-oil temperature (TOT) models that will be used in the application--the ASU HST/TOT models and the ANSI models--are presented. Brief validations of the ASU models are presented, showing that the ASU models are accurate in simulating the thermal processes of the transformers. For this production grade application, both the ANSI and the ASU models are built and tested to select the most appropriate models to be used in the dynamic loading calculations. An existing application to build and select the TOT model was used as a starting point for the enhancements developed in this work. These enhancements include: Adding the ability to develop HST models to the existing application, Adding metrics to evaluate the models accuracy and selecting which model will be used in dynamic loading calculation Adding the capability to perform dynamic loading calculations, Production of a maximum dynamic load profile that the transformer can tolerate without acceleration of the insulation aging, Provide suitable output (plots and text) for the results of the dynamic loading calculation. Other challenges discussed include: modification to the input data format, data-quality control, cooling mode estimation. Efforts to overcome these challenges are discussed in this work.
ContributorsLiu, Yi (Author) / Tylavksy, Daniel J (Thesis advisor) / Karady, George G. (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2011
Description
Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java code that runs within a JVM to interoperate with applications or libraries that are written in other languages and compiled to the host CPU ISA. JNI plays an important role in embedded system as it provides a mechanism to interact with libraries specific to the platform. This thesis addresses the overhead incurred in the JNI due to reflection and serialization when objects are accessed on android based mobile devices. It provides techniques to reduce this overhead. It also provides an API to access objects through its reference through pinning its memory location. The Android emulator was used to evaluate the performance of these techniques and we observed that there was 5 - 10 % performance gain in the new Java Native Interface.
ContributorsChandrian, Preetham (Author) / Lee, Yann-Hang (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis pursues a method to deregulate the electric distribution system and provide support to distributed renewable generation. A locational marginal price is used to determine prices across a distribution network in real-time. The real-time pricing may provide benefits such as a reduced electricity bill, decreased peak demand, and lower emissions. This distribution locational marginal price (D-LMP) determines the cost of electricity at each node in the electrical network. The D-LMP is comprised of the cost of energy, cost of losses, and a renewable energy premium. The renewable premium is an adjustable function to compensate `green' distributed generation. A D-LMP is derived and formulated from the PJM model, as well as several alternative formulations. The logistics and infrastructure an implementation is briefly discussed. This study also takes advantage of the D-LMP real-time pricing to implement distributed storage technology. A storage schedule optimization is developed using linear programming. Day-ahead LMPs and historical load data are used to determine a predictive optimization. A test bed is created to represent a practical electric distribution system. Historical load, solar, and LMP data are used in the test bed to create a realistic environment. A power flow and tabulation of the D-LMPs was conducted for twelve test cases. The test cases included various penetrations of solar photovoltaics (PV), system networking, and the inclusion of storage technology. Tables of the D-LMPs and network voltages are presented in this work. The final costs are summed and the basic economics are examined. The use of a D-LMP can lower costs across a system when advanced technologies are used. Storage improves system costs, decreases losses, improves system load factor, and bolsters voltage. Solar energy provides many of these same attributes at lower penetrations, but high penetrations have a detrimental effect on the system. System networking also increases these positive effects. The D-LMP has a positive impact on residential customer cost, while greatly increasing the costs for the industrial sector. The D-LMP appears to have many positive impacts on the distribution system but proper cost allocation needs further development.
ContributorsKiefer, Brian Daniel (Author) / Heydt, Gerald T (Thesis advisor) / Shunk, Dan (Committee member) / Hedman, Kory (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis describes a synthetic task environment, CyberCog, created for the purposes of 1) understanding and measuring individual and team situation awareness in the context of a cyber security defense task and 2) providing a context for evaluating algorithms, visualizations, and other interventions that are intended to improve cyber situation awareness. CyberCog provides an interactive environment for conducting human-in-loop experiments in which the participants of the experiment perform the tasks of a cyber security defense analyst in response to a cyber-attack scenario. CyberCog generates the necessary performance measures and interaction logs needed for measuring individual and team cyber situation awareness. Moreover, the CyberCog environment provides good experimental control for conducting effective situation awareness studies while retaining realism in the scenario and in the tasks performed.
ContributorsRajivan, Prashanth (Author) / Femiani, John (Thesis advisor) / Cooke, Nancy J. (Thesis advisor) / Lindquist, Timothy (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2011
Description
With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications of compressive sensing and sparse representation with regards to image enhancement, restoration and classication. The first application deals with image Super-Resolution through compressive sensing based sparse representation. A novel framework is developed for understanding and analyzing some of the implications of compressive sensing in reconstruction and recovery of an image through raw-sampled and trained dictionaries. Properties of the projection operator and the dictionary are examined and the corresponding results presented. In the second application a novel technique for representing image classes uniquely in a high-dimensional space for image classification is presented. In this method, design and implementation strategy of the image classification system through unique affine sparse codes is presented, which leads to state of the art results. This further leads to analysis of some of the properties attributed to these unique sparse codes. In addition to obtaining these codes, a strong classier is designed and implemented to boost the results obtained. Evaluation with publicly available datasets shows that the proposed method outperforms other state of the art results in image classication. The final part of the thesis deals with image denoising with a novel approach towards obtaining high quality denoised image patches using only a single image. A new technique is proposed to obtain highly correlated image patches through sparse representation, which are then subjected to matrix completion to obtain high quality image patches. Experiments suggest that there may exist a structure within a noisy image which can be exploited for denoising through a low-rank constraint.
ContributorsKulkarni, Naveen (Author) / Li, Baoxin (Thesis advisor) / Ye, Jieping (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
In recent years, the length of time people use and keep belongings has decreased. With the acceptance of short-lived furniture and inexpensive replacements, the American mentality has shifted to thinking that discarding furniture is normal, often in the guise of recycling. Americans are addicted to landfills. The high cost of landfill real estate and other considerable ecological impacts created by the manufacturing of furniture should persuade people to give their belongings a longer life, but in reality, furniture is often prematurely discarded. This grounded theory study takes a multi-method approach to analyze why some types of furniture are kept longer and to theorize about new ways to design and sell furniture that lasts well past its warranty. Case studies bring new insight into designer intention, manufacturer intent, the world of auction-worthy collectables and heirlooms, why there is a booming second-hand furniture market and the growing importance of informed interior designers and architects who specify or help clients choose interior furnishings. An environmental life cycle assessment compares how the length of furniture life affects environmental impacts. A product's life could continue for generations if properly maintained. Designers and manufacturers hoping to promote longevity can apply the conclusions of this report in bringing new pieces to the market that have a much longer life span. This study finds areas of opportunity that promote user attachment, anticipate future repurposing, and provide services. This thinking envisions a paradigm for furniture that can re-invent itself over multiple generations of users, and ultimately lead to a new wave of desirable heirloom furniture.
ContributorsIngham, Sarah (Author) / White, Philip (Thesis advisor) / Wolf, Peter (Committee member) / Underhill, Michael (Committee member) / Arizona State University (Publisher)
Created2011
Description
Individuals' experiences, environment, and education greatly impact their entire being. Similarly, a designer is affected by these elements, which impacts how, what and why they design. In order for design education to generate designers who are more socially aware problem solvers, that education must introduce complex social matters and not just design skills. Traditionally designers learned through apprenticing a master. Most design education has moved away from this traditional model and has begun incorporating a well-rounded program of study, yet there are still more improvements to be made. This research proposes a new Integrated Transformational Experience Model, ITEM, for design education which will be rooted in sustainability, cultural integration, social embeddedness, and discipline collaboration. The designer will be introduced to new ideas and experiences from the immersion of current social issues where they will gain experience creating solutions to global problems enabling them to become catalysts of change. This research is based on interviews with industrial design students to gain insights, benefits and drawbacks of the current model of design education. This research will expand on the current model for design education, combining new ideas that will shed light on the future of design disciplines through the education and motivation of designers. The desired outcome of this study is to incorporate hands on learning through social issues in design classrooms, identify ways to educate future problem solvers, and inspire more research on this issue.
ContributorsWingate, Andrea (Author) / Takamura, John (Thesis advisor) / Stamm, Jill (Committee member) / Bender, Diane (Committee member) / Arizona State University (Publisher)
Created2011
Description
Interior design continues to re-define itself as a discipline that presents designers with new problems that require innovative solutions. This is particularly true in the case in office design. The transformation of the office environment from the standard bullpen configuration to today's dynamic, flexible, and open floor plans has required new design methodologies that incorporate tools and technologies that are readily available to interior designers. Today, increased use of teams in the workplace challenges interior designers to create environments that accommodate both group and individual tasks (Brill, Weidermann & BOSTI associates, 2001). Collaboration has received considerable attention as organizations focus on productivity and reducing costs to compete in a global economy (Hassanain, 2006). Designers and architects should learn to create environments that respond to dynamic, moveable, and flexible work methods. This web-based research study explores the use of pattern language as a new tool for designing collaborative work environments. In 1977, Christopher Alexander and his associates developed `Pattern language' (Alexander, Ishikawa & Silverstein, 1977) as a design formulation methodology. It consists of a series of interrelated physical elements combined to create a framework for design solutions. This pattern language tool for collaborative work environments was created based on research by Lori Anthony (2001). This study further builds upon current trends and research in collaborative work environments. The researcher conducted a pilot test by sending the web-based tool and an online questionnaire to all graduate students and faculty members in the fields of interior design and healthcare and healing environment (HHE). After testing its validity in The Design School at Arizona State University, the same tool and questionnaire was sent to the employees of one of the leading architecture and interior design firms in Phoenix, AZ. The results showed that among those design professionals surveyed, the majority believe pattern language could be a valuable design tool. The insights obtained from this study will provide designers, architects, and facility managers with a new design tool to aid in creating effective collaborative spaces in a work environment.
ContributorsSangoi, Deepika (Author) / Bender, Diane (Thesis advisor) / Brandt, Beverly (Committee member) / Heywood, William (Committee member) / Arizona State University (Publisher)
Created2011
Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
Description
In this thesis the performance of a Hybrid AC System (HACS) is modeled and optimized. The HACS utilizes solar photovoltaic (PV) panels to help reduce the demand from the utility during peak hours. The system also includes an ice Thermal Energy Storage (TES) tank to accumulate cooling energy during off-peak hours. The AC runs continuously on grid power during off-peak hours to generate cooling for the house and to store thermal energy in the TES. During peak hours, the AC runs on the power supplied from the PV, and cools the house along with the energy stored in the TES. A higher initial cost is expected due to the additional components of the HACS (PV and TES), but a lower operational cost due to higher energy efficiency, energy storage and renewable energy utilization. A house cooled by the HACS will require a smaller size AC unit (about 48% less in the rated capacity), compared to a conventional AC system. To compare the cost effectiveness of the HACS with a regular AC system, time-of-use (TOU) utility rates are considered, as well as the cost of the system components and the annual maintenance. The model shows that the HACS pays back its initial cost of $28k in about 6 years with an 8% APR, and saves about $45k in total cost when compared to a regular AC system that cools the same house for the same period of 6 years.
ContributorsJubran, Sadiq (Author) / Phelan, Patrick (Thesis advisor) / Calhoun, Ronald (Committee member) / Trimble, Steve (Committee member) / Arizona State University (Publisher)
Created2011