Matching Items (54)
Description
Suction stabilized floats have been implemented into a variety of applications such as supporting wind turbines in off-shore wind farms and for stabilizing cargo ships. This thesis proposes an alternative use for the technology in creating a system of suction stabilized floats equipped with real time location modules to help first responders establish a localized coordinate system to assist in rescues. The floats create a stabilized platform for each anchor module due to the inverse slack tank effect established by the inner water chamber. The design of the float has also been proven to be stable in most cases of amplitudes and frequencies ranging from 0 to 100 except for when the frequency ranges from 23 to 60 Hz for almost all values of the amplitude. The modules in the system form a coordinate grid based off the anchors that can track the location of a tag module within the range of the system using ultra-wideband communications. This method of location identification allows responders to use the system in GPS denied environments. The system can be accessed through an Android app with Bluetooth communications in close ranges or through internet of things (IoT) using a module as a listener, a Raspberry Pi and an internet source. The system has proven to identify the location of the tag in moderate ranges with an approximate accuracy of the tag location being 15 cm.
ContributorsDye, Michaela (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2020
Description
Solar photovoltaic (PV) generation has seen significant growth in 2021, with an increase of around 22% and exceeding 1000 TWh. However, this has also led to reliability and durability issues, particularly potential induced degradation (PID), which can reduce module output by up to 30%. This study uses cell- and module-level analysis to investigate the impact of superstrate, encapsulant, and substrate on PID.The influence of different substrates and encapsulants is studied using one-cell modules, showing that substrates with poor water-blocking properties can worsen PID, and encapsulants with lower volumetric resistance can conduct easily under damp conditions, enabling PID mechanisms (results show maximum degradation of 9%). Applying an anti-soiling coating on the front glass (superstrate) reduces PID by nearly 53%. Typical superstrates have sodium which accelerates the PID process, and therefore, using such coatings can lessen the PID problem.
At the module level, the study examines the influence of weakened interface adhesion strengths in traditional Glass-Backsheet (GB) and emerging Glass-Glass (GG) (primarily bifacial modules) constructions. The findings show nearly 64% more power degradation in GG modules than in GB. Moreover, the current methods for detecting PID use new modules, which can give inaccurate information instead of DH-stressed modules for PID testing, as done in this work.
A comprehensive PID susceptibility analysis for multiple fresh bifacial constructions shows significant degradation from 20 to 50% in various constructions. The presence of glass as the substrate exacerbates the PID problem due to more ionic activity available from the two glass sides. Recovery experiments are also conducted to understand the extent of the PID issue.
Overall, this study identifies, studies, and explains the impact of superstrate, substrate, and encapsulant on the underlying PID mechanisms. Various pre- and post-stress characterization tests, including light and dark current-voltage (I-V) tests, electroluminescence (EL) imaging, infrared (IR) imaging, and UV fluorescence (UVF) imaging, are used to evaluate the findings. This study is significant as it provides insights into the PID issues in solar PV systems, which can help improve their performance and reliability.
ContributorsMahmood, Farrukh ibne (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Oh, Jaewon (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2023
Description
In the past 10 to 15 years, there has been a tremendous increase in the amount of photovoltaic (PV) modules being both manufactured and installed in the field. Power plants in the hundreds of megawatts are continuously being turned online as the world turns toward greener and sustainable energy. Due to this fact and to calculate LCOE (levelized cost of energy), it is understandably becoming more important to comprehend the behavior of these systems as a whole by calculating two key data: the rate at which modules are degrading in the field; the trend (linear or nonlinear) in which the degradation is occurring. As opposed to periodical in field intrusive current-voltage (I-V) measurements, non-intrusive measurements are preferable to obtain these two key data since owners do not want to lose money by turning their systems off, as well as safety and breach of installer warranty terms. In order to understand the degradation behavior of PV systems, there is a need for highly accurate performance modeling. In this thesis 39 commercial PV power plants from the hot-dry climate of Arizona are analyzed to develop an understanding on the rate and trend of degradation seen by crystalline silicon PV modules. A total of three degradation rates were calculated for each power plant based on three methods: Performance Ratio (PR), Performance Index (PI), and raw kilowatt-hour. These methods were validated from in field I-V measurements obtained by Arizona State University Photovoltaic Reliability Lab (ASU-PRL). With the use of highly accurate performance models, the generated degradation rates may be used by the system owners to claim a warranty from PV module manufactures or other responsible parties.
ContributorsRaupp, Christopher (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2016
Description
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.
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.
ContributorsBoppana, Sravanthi (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2015
Description
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.
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.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Characterization and analysis of long term field aged photovoltaic modules and encapsulant materials
Description
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.
ContributorsChicca, Matthew (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2015
Description
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.
ContributorsMartínez, Luis, active 1995 (Author) / Rajadas, John (Thesis advisor) / Kannan, Arunachala (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2016
Description
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.
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.
ContributorsDonovan, Benjamin (Author) / Rajadas, John (Thesis advisor) / Kannan, Arunachala (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2016
Description
The primary goal of this thesis work is to determine the activation energy for encapsulant browning reaction of photovoltaic (PV) modules using outdoor field degradation data and indoor accelerated degradation data. For the outdoor field data, six PV modules fielded in Arizona (hot climate) over 21 years and four PV modules fielded in New York (cold climate) over 18 years have been analyzed. All the ten modules were manufactured by the same manufacturer with glass/EVA/cell/EVA/back sheet construction. The activation energy for the encapsulant browning is calculated using the degradation rates of short-circuit current (Isc, the response parameter), weather data (temperature, humidity, and UV, the stress parameters) and different empirical rate models such as Arrhenius, Peck, Klinger and modified Peck models. For the indoor accelerated data, three sets of mini-modules with the same construction/manufacturer as that of the outdoor fielded modules were subjected indoor accelerated weathering stress and the test data were analyzed. The indoor accelerated test was carried out in a weathering chamber at the chamber temperature of 20°C, chamber relative humidity of 65%, and irradiance of 1 W/m2 at 340nm using a xenon arc lamp. Typically, to obtain activation energy, the test samples are stressed at two (or more) temperatures in two (or more) chambers. However, in this work, it has been attempted to do the acceleration testing of eight mini-modules at multiple temperatures using a single chamber. Multiple temperatures in a single chamber were obtained using thermal insulators on the back of the mini-modules. Depending on the thickness of the thermal insulators with constant solar gain from the xenon lamp, different temperatures on the test samples were achieved using a single weathering chamber. The Isc loss and temperature of the mini-modules were continuously monitored using a data logger. Also, the mini-modules were taken out every two weeks and various characterization tests such as IV, QE, UV fluorescence and reflectance were carried out. Activation energy from the indoor accelerated tests was calculated using the short circuit current degradation rate and operating temperatures of the mini-modules. The activation energy for the encapsulant browning obtained from the outdoor field data and the indoor accelerated data are compared and analyzed in this work.
ContributorsVeerendra Kumar, Deepak Jain (Author) / Tamizhmani, Govindasamy (Committee member) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2016
Description
As the photovoltaic (PV) power plants age in the field, the PV modules degrade and generate visible and invisible defects. A defect and statistical degradation rate analysis of photovoltaic (PV) power plants is presented in two-part thesis. The first part of the thesis deals with the defect analysis and the second part of the thesis deals with the statistical degradation rate analysis. In the first part, a detailed analysis on the performance or financial risk related to each defect found in multiple PV power plants across various climatic regions of the USA is presented by assigning a risk priority number (RPN). The RPN for all the defects in each PV plant is determined based on two databases: degradation rate database; defect rate database. In this analysis it is determined that the RPN for each plant is dictated by the technology type (crystalline silicon or thin-film), climate and age. The PV modules aging between 3 and 19 years in four different climates of hot-dry, hot-humid, cold-dry and temperate are investigated in this study.
In the second part, a statistical degradation analysis is performed to determine if the degradation rates are linear or not in the power plants exposed in a hot-dry climate for the crystalline silicon technologies. This linearity degradation analysis is performed using the data obtained through two methods: current-voltage method; metered kWh method. For the current-voltage method, the annual power degradation data of hundreds of individual modules in six crystalline silicon power plants of different ages is used. For the metered kWh method, a residual plot analysis using Winters’ statistical method is performed for two crystalline silicon plants of different ages. The metered kWh data typically consists of the signal and noise components. Smoothers remove the noise component from the data by taking the average of the current and the previous observations. Once this is done, a residual plot analysis of the error component is performed to determine the noise was successfully separated from the data by proving the noise is random.
In the second part, a statistical degradation analysis is performed to determine if the degradation rates are linear or not in the power plants exposed in a hot-dry climate for the crystalline silicon technologies. This linearity degradation analysis is performed using the data obtained through two methods: current-voltage method; metered kWh method. For the current-voltage method, the annual power degradation data of hundreds of individual modules in six crystalline silicon power plants of different ages is used. For the metered kWh method, a residual plot analysis using Winters’ statistical method is performed for two crystalline silicon plants of different ages. The metered kWh data typically consists of the signal and noise components. Smoothers remove the noise component from the data by taking the average of the current and the previous observations. Once this is done, a residual plot analysis of the error component is performed to determine the noise was successfully separated from the data by proving the noise is random.
ContributorsSundarajan, Prasanna (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2016