Testing was conducted for a solar assisted water heater and conventional all electric water heater for the purpose of investigating the advantages of utilizing solar energy to heat up water. The testing conducted simulated a four person household living in the Phoenix, Arizona region. With sensors and a weather station, data was gathered and analyzed for the water heaters. Performance patterns were observed that correlated to ambient conditions and functionality of the solar assisted water heater. This helped better understand how the solar water heater functioned and how it may continue to function. The testing for the solar assisted water heater was replicated with the all-electric water heater. One to one analyzes was conducted for comparison. The efficiency and advantages were displayed by the solar assisted water heater having a 61% efficiency. Performance parameters were calculated for the solar assisted water heater and it showed how accurate certified standards are. The results showed 8% difference in performance, but differed in energy savings. This further displayed the effects of uncontrollable ambient conditions and the effects of different testing conditions.

With the need to address the world's growing energy demand, many new

alternative and renewable energy sources are being researched and developed. Many

of these technologies are in their infancy, still being too inefficient or too costly to

implement on a large scale. This list of alternative energies include biofuels,

geothermal power, solar energy, wind energy and hydroelectric power. This thesis

focuses on developing a concentrating solar thermal energy unit for the application

of an on-demand hot water system with phase change material. This system already

has a prototype constructed and needs refinement in several areas in order to

increase its efficiency to determine if the system could ever reach a point of

feasibility in a residential application. Having put additional control refining

systems on the solar water heat collector, it can be deduced that the efficiency has

increased. However, due to limited testing and analysis it is undetermined just how

much the efficiency of the system has increased. At minimum, the capabilities of the

research platform have dramatically increased, allowing future research to more

accurately study the dynamics of the system as well as conduct studies in more

targeted areas of engineering. In this aspect, the thesis was successful.

The complicated, unpredictable, and often chaotic hot water usage pattern of typical households severely limits the effectiveness and efficiency of traditional solar hot water heater systems. Similar to large scale concentrating solar power plants, the use of thermal energy storage techniques to store collected solar energy as latent heat has the potential to improve the efficiency of solar hot water systems. Rather than being used to produce steam to generate electricity, the stored thermal energy would be used to heat water on-demand well after the sun sets. The scope of this thesis was to design, analyze, build, and test a proof of concept prototype for an on-demand solar water heater for residential use with latent heat thermal energy storage. The proof of concept system will be used for future research and can be quickly reconfigured making it ideal for use as a test bed. This thesis outlines the analysis, design, and testing processes used to model, build, and evaluate the performance of the prototype system.

The prototype system developed to complete this thesis was designed using systems engineering principles and consists of several main subsystems. These subsystems include a parabolic trough concentrating solar collector, a phase change material reservoir including heat exchangers, a heat transfer fluid reservoir, and a plumbing system. The system functions by absorbing solar thermal energy in a heat transfer fluid using the solar collector and transferring the absorbed thermal energy to the phase change material for storage. The system was analyzed using a mathematical model created in MATLAB and experimental testing was used to verify that the system functioned as designed. The mathematical model was designed to be adaptable for evaluating different system configurations for future research. The results of the analysis as well as the experimental tests conducted, verify that the proof of concept system is functional and capable of producing hot water using stored thermal energy. This will allow the system to function as a test bed for future research and long-term performance testing to evaluate changes in the performance of the phase change material over time. With additional refinement the prototype system has the potential to be developed into a commercially viable product for use in residential homes.

Photovoltaic (PV) module degradation is a well-known issue, however understanding the mechanistic pathways in which modules degrade is still a major task for the PV industry. In order to study the mechanisms responsible for PV module degradation, the effects of these degradation mechanisms must be quantitatively measured to determine the severity of each degradation mode. In this thesis multiple modules from three climate zones (Arizona, California and Colorado) were investigated for a single module glass/polymer construction (Siemens M55) to determine the degree to which they had degraded, and the main factors that contributed to that degradation. To explain the loss in power, various nondestructive and destructive techniques were used to indicate possible causes of loss in performance. This is a two-part thesis. Part 1 presents non-destructive test results and analysis and Part 2 presents destructive test results and analysis.

Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.

In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.

Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.

Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.

This is a two-part thesis.

Part 1 presents an approach for working towards the development of a standardized artificial soiling method for laminated photovoltaic (PV) cells or mini-modules. Construction of an artificial chamber to maintain controlled environmental conditions and components/chemicals used in artificial soil formulation is briefly explained. Both poly-Si mini-modules and a single cell mono-Si coupons were soiled and characterization tests such as I-V, reflectance and quantum efficiency (QE) were carried out on both soiled, and cleaned coupons. From the results obtained, poly-Si mini-modules proved to be a good measure of soil uniformity, as any non-uniformity present would not result in a smooth curve during I-V measurements. The challenges faced while executing reflectance and QE characterization tests on poly-Si due to smaller size cells was eliminated on the mono-Si coupons with large cells to obtain highly repeatable measurements. This study indicates that the reflectance measurements between 600-700 nm wavelengths can be used as a direct measure of soil density on the modules.

Part 2 determines the most dominant failure modes of field aged PV modules using experimental data obtained in the field and statistical analysis, FMECA (Failure Mode, Effect, and Criticality Analysis). The failure and degradation modes of about 744 poly-Si glass/polymer frameless modules fielded for 18 years under the cold-dry climate of New York was evaluated. Defect chart, degradation rates (both string and module levels) and safety map were generated using the field measured data. A statistical reliability tool, FMECA that uses Risk Priority Number (RPN) is used to determine the dominant failure or degradation modes in the strings and modules by means of ranking and prioritizing the modes. This study on PV power plants considers all the failure and degradation modes from both safety and performance perspectives.

The indoor and outdoor soiling studies were jointly performed by two Masters Students, Sravanthi Boppana and Vidyashree Rajasekar. This thesis presents the indoor soiling study, whereas the other thesis presents the outdoor soiling study. Similarly, the statistical risk analyses of two power plants (model J and model JVA) were jointly performed by these two Masters students. Both power plants are located at the same cold-dry climate, but one power plant carries framed modules and the other carries frameless modules. This thesis presents the results obtained on the frameless modules.

This is a two-part thesis:

Part 1 characterizes soiling losses using various techniques to understand the effect of soiling on photovoltaic modules. The higher the angle of incidence (AOI), the lower will be the photovoltaic (PV) module performance. Our research group has already reported the AOI investigation for cleaned modules of five different technologies with air/glass interface. However, the modules that are installed in the field would invariably develop a soil layer with varying thickness depending on the site condition, rainfall and tilt angle. The soiled module will have the air/soil/glass interface rather than air/glass interface. This study investigates the AOI variations on soiled modules of five different PV technologies. It is demonstrated that AOI effect is inversely proportional to the soil density. In other words, the power or current loss between clean and soiled modules would be much higher at a higher AOI than at a lower AOI leading to excessive energy production loss of soiled modules on cloudy days, early morning hours and late afternoon hours. Similarly, the spectral influence of soil on the performance of the module was investigated through reflectance and transmittance measurements. It was observed that the reflectance and transmittances losses vary linearly with soil density variation and the 600-700 nm band was identified as an ideal band for soil density measurements.

Part 2 of this thesis performs statistical risk analysis for a power plant through FMECA (Failure Mode, Effect, and Criticality Analysis) based on non-destructive field techniques and count data of the failure modes. Risk Priority Number is used for the grading guideline for criticality analysis. The analysis was done on a 19-year-old power plant in cold-dry climate to identify the most dominant failure and degradation modes. In addition, a comparison study was done on the current power plant (framed) along with another 18-year-old (frameless) from the same climate zone to understand the failure modes for cold-dry climatic condition.

The prevalence of renewable generation will increase in the next several decades and offset conventional generation more and more. Yet this increase is not coming without challenges. Solar, wind, and even some water resources are intermittent and unpredictable, and thereby create scheduling challenges due to their inherent “uncontrolled” nature. To effectively manage these distributed renewable assets, new control algorithms must be developed for applications including energy management, bridge power, and system stability. This can be completed through a centralized control center though efforts are being made to parallel the control architecture with the organization of the renewable assets themselves—namely, distributed controls. Building energy management systems are being employed to control localized energy generation, storage, and use to reduce disruption on the net utility load. One such example is VOLTTRONTM, an agent-based platform for building energy control in real time. In this thesis, algorithms developed in VOLTTRON simulate a home energy management system that consists of a solar PV array, a lithium-ion battery bank, and the grid. Dispatch strategies are implemented to reduce energy charges from overall consumption ($/kWh) and demand charges ($/kW). Dispatch strategies for implementing storage devices are tuned on a month-to-month basis to provide a meaningful economic advantage under simulated scenarios to explore algorithm sensitivity to changing external factors. VOLTTRON agents provide automated real-time optimization of dispatch strategies to efficiently manage energy supply and demand, lower consumer costs associated with energy usage, and reduce load on the utility grid.

Research was conducted to observe the effect of Number of Transparent Covers and Refractive Index on performance of a domestic Solar Water heating system. The enhancement of efficiency for solar thermal system is an emerging challenge. The knowledge gained from this research will enable to optimize the number of transparent covers and refractive index prior to develop a solar water heater with improved optical efficiency and thermal efficiency for the collector. Numerical simulation is conducted on the performance of the liquid flat plate collector for July 21st and October 21st from 8 am to 4 pm with different refractive index values 1.1, 1.4, 1.7 and different numbers of transparent covers (0-3). In order to accomplish the proposed method the formulation and solutions are executed using simple software MATLAB. The result demonstrates efficiency of flat plate collector increases with the increase of number of covers. The performance of collector decreases when refractive index is higher. The improved useful heat gain is obtained when number of cover used is 3 and refractive index is 1.1.

This study evaluates the potential profitability and environmental benefit available by providing renewable energy from solar- or wind-generated sources to electric vehicle drivers at public charging stations, also known as electric vehicle service equipment (EVSE), in the U.S. Past studies have shown above-average interest in renewable energy by drivers of plug-in electric vehicles (PEVs), though no study has evaluated the profitability and environmental benefit of selling renewable energy to PEV drivers at public EVSE. Through an online survey of 203 U.S.-wide PEV owners and lessees, information was collected on (1) current PEV and EVSE usage, (2) potential willingness to pay (WTP) for upgrading their charge event to renewable energy, and (3) usage of public EVSE if renewable energy was offered. The choice experiment survey method was used to avoid bias known to occur when directly asking for WTP. Sixty percent of the participants purchased their PEVs due to environmental concerns. The survey results indicate a 506% increase in the usage of public pay-per-use EVSE if renewable energy was offered and a mean WTP to upgrade to renewable energy of $0.61 per hour for alternating current (AC) Level 2 EVSE and $1.82 for Direct Current (DC) Fast Chargers (DCFC). Based on data from the 2013 second quarter (2Q) report of The EV Project, which uses the Blink public EVSE network, this usage translates directly to an annual gross income increase of 668% from the original $1.45 million to $11.1 million. Blink would see an annual cost of $16,005 per year for the acquisition of the required renewable energy as renewable energy credits (RECs). Excluding any profit seen purely from the raise in usage, $3.8 million in profits would be gained directly from the sale of renewable energy. Relative to a gasoline-powered internal combustion engine passenger vehicle, greenhouse gas (GHG) emissions are 42% less for the U.S. average blend grid electricity-powered electric vehicle and 99.997% less when wind energy is used. Powering all Blink network charge events with wind energy would reduce the annualized 2Q 2013 GHG emissions of 1,589 metric tons CO2 / yr to 125 kg CO2 / yr, which is the equivalent of removing 334 average U.S. gasoline passenger cars from the road. At the increased usage, 8,031 metric tons CO2 / yr would be prevented per year or the equivalent of the elimination of 1,691 average U.S. passenger cars. These economic and environmental benefits will increase as PEV ownership increases over time.