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- All Subjects: engineering
- Genre: Masters Thesis
- Creators: Tamizhmani, Govindasamy
- Creators: Redkar, Sangram
- Resource Type: Text
Description
The objective of this thesis was to compare various approaches for classification of the `good' and `bad' parts via non-destructive resonance testing methods by collecting and analyzing experimental data in the frequency and time domains. A Laser Scanning Vibrometer was employed to measure vibrations samples in order to determine the spectral characteristics such as natural frequencies and amplitudes. Statistical pattern recognition tools such as Hilbert Huang, Fisher's Discriminant, and Neural Network were used to identify and classify the unknown samples whether they are defective or not. In this work, a Finite Element Analysis software packages (ANSYS 13.0 and NASTRAN NX8.0) was used to obtain estimates of resonance frequencies in `good' and `bad' samples. Furthermore, a system identification approach was used to generate Auto-Regressive-Moving Average with exogenous component, Box-Jenkins, and Output Error models from experimental data that can be used for classification
ContributorsJameel, Osama (Author) / Redkar, Sangram (Thesis advisor) / Arizona State University (Publisher)
Created2013
Description
The object of this study was a 26 year old residential Photovoltaic (PV) monocrystalline silicon (c-Si) power plant, called Solar One, built by developer John F. Long in Phoenix, Arizona (a hot-dry field condition). The task for Arizona State University Photovoltaic Reliability Laboratory (ASU-PRL) graduate students was to evaluate the power plant through visual inspection, electrical performance, and infrared thermography. The purpose of this evaluation was to measure and understand the extent of degradation to the system along with the identification of the failure modes in this hot-dry climatic condition. This 4000 module bipolar system was originally installed with a 200 kW DC output of PV array (17 degree fixed tilt) and an AC output of 175 kVA. The system was shown to degrade approximately at a rate of 2.3% per year with no apparent potential induced degradation (PID) effect. The power plant is made of two arrays, the north array and the south array. Due to a limited time frame to execute this large project, this work was performed by two masters students (Jonathan Belmont and Kolapo Olakonu) and the test results are presented in two masters theses. This thesis presents the results obtained on the north array and the other thesis presents the results obtained on the south array. The resulting study showed that PV module design, array configuration, vandalism, installation methods and Arizona environmental conditions have had an effect on this system's longevity and reliability. Ultimately, encapsulation browning, higher series resistance (potentially due to solder bond fatigue) and non-cell interconnect ribbon breakages outside the modules were determined to be the primary causes for the power loss.
ContributorsBelmont, Jonathan (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Henderson, Mark (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2013
Description
Potential induced degradation (PID) due to high system voltages is one of the major degradation mechanisms in photovoltaic (PV) modules, adversely affecting their performance due to the combined effects of the following factors: system voltage, superstrate/glass surface conductivity, encapsulant conductivity, silicon nitride anti-reflection coating property and interface property (glass/encapsulant; encapsulant/cell; encapsulant/backsheet). Previous studies carried out at ASU's Photovoltaic Reliability Laboratory (ASU-PRL) showed that only negative voltage bias (positive grounded systems) adversely affects the performance of commonly available crystalline silicon modules. In previous studies, the surface conductivity of the glass surface was obtained using either conductive carbon layer extending from the glass surface to the frame or humidity inside an environmental chamber. This thesis investigates the influence of glass surface conductivity disruption on PV modules. In this study, conductive carbon was applied only on the module's glass surface without extending to the frame and the surface conductivity was disrupted (no carbon layer) at 2cm distance from the periphery of frame inner edges. This study was carried out under dry heat at two different temperatures (60 °C and 85 °C) and three different negative bias voltages (-300V, -400V, and -600V). To replicate closeness to the field conditions, half of the selected modules were pre-stressed under damp heat for 1000 hours (DH 1000) and the remaining half under 200 hours of thermal cycling (TC 200). When the surface continuity was disrupted by maintaining a 2 cm gap from the frame to the edge of the conductive layer, as demonstrated in this study, the degradation was found to be absent or negligibly small even after 35 hours of negative bias at elevated temperatures. This preliminary study appears to indicate that the modules could become immune to PID losses if the continuity of the glass surface conductivity is disrupted at the inside boundary of the frame. The surface conductivity of the glass, due to water layer formation in a humid condition, close to the frame could be disrupted just by applying a water repelling (hydrophobic) but high transmittance surface coating (such as Teflon) or modifying the frame/glass edges with water repellent properties.
ContributorsTatapudi, Sai Ravi Vasista (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2012
Description
Photovoltaic (PV) systems are one of the next generation's renewable energy sources for our world energy demand. PV modules are highly reliable. However, in polluted environments, over time, they will collect grime and dust. There are also limited field data studies about soiling losses on PV modules. The study showed how important it is to investigate the effect of tilt angle on soiling. The study includes two sets of mini-modules. Each set has 9 PV modules tilted at 0, 5, 10, 15, 20, 23, 30, 33 and 40°. The first set called "Cleaned" was cleaned every other day. The second set called "Soiled" was never cleaned after the first day. The short circuit current, a measure of irradiance, and module temperature was monitored and recorded every two minutes over three months (January-March 2011). The data were analyzed to investigate the effect of tilt angle on daily and monthly soiling, and hence transmitted solar insolation and energy production by PV modules. The study shows that during the period of January through March 2011 there was an average loss due to soiling of approximately 2.02% for 0° tilt angle. Modules at tilt anlges 23° and 33° also have some insolation losses but do not come close to the module at 0° tilt angle. Tilt anlge 23° has approximately 1.05% monthly insolation loss, and 33° tilt angle has an insolation loss of approximately 0.96%. The soiling effect is present at any tilt angle, but the magnitude is evident: the flatter the solar module is placed the more energy it will lose.
ContributorsCano Valero, José (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Madakannan, Arunachalanadar (Committee member) / Macia, Narciso (Committee member) / Arizona State University (Publisher)
Created2011
Description
The objective of this work is to develop a Stop-Rotor Multimode UAV. This UAV is capable of vertical take-off and landing like a helicopter and can convert from a helicopter mode to an airplane mode in mid-flight. Thus, this UAV can hover as a helicopter and achieve high mission range of an airplane. The stop-rotor concept implies that in mid-flight the lift generating helicopter rotor stops and rotates the blades into airplane wings. The thrust in airplane mode is then provided by a pusher propeller. The aircraft configuration presents unique challenges in flight dynamics, modeling and control. In this thesis a mathematical model along with the design and simulations of a hover control will be presented. In addition, the discussion of the performance in fixed-wing flight, and the autopilot architecture of the UAV will be presented. Also presented, are some experimental "conversion" results where the Stop-Rotor aircraft was dropped from a hot air balloon and performed a successful conversion from helicopter to airplane mode.
ContributorsVargas-Clara, Alvaro (Author) / Redkar, Sangram (Thesis advisor) / Macia, Narciso (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2011
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
The sensor industry is a growing industry that has been predicted by Allied Market Research to be a multi-billion industry by 2022. One of the many key drives behind this rapid growth in the sensor industry is the increase incorporation of sensors into portable electrical devices. The value for sensor technologies are increased when the sensors are developed into innovative measuring system for application uses in the Aerospace, Defense, and Healthcare industries. While sensors are not new, their increased performance, size reduction, and decrease in cost has opened the door for innovative sensor combination for portable devices that could be worn or easily moved around. With this opportunity for further development of sensor use through concept engineering development, three concept projects for possible innovative portable devices was undertaken in this research. One project was the development of a pulse oximeter devise with fingerprint recognition. The second project was prototyping a portable Bluetooth strain gage monitoring system. The third project involved sensors being incorporated onto flexible printed circuit board (PCB) for improved comfort of wearable devices. All these systems were successfully tested in lab.
ContributorsNichols, Kevin William (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Brad (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2018
Description
Recent research and study have showed the potential of auto-parametric system in controlling stability and parametric resonance. In this project, two different designs for auto-parametrically excited mass-spring-damper systems were studied. The theoretical models were developed to describe the behavior of the systems, and simulation models were constructed to validate the analytical results. The error between simulation and theoretical results was within 2%. Both theoretical and simulation results showed that the implementation of auto-parametric system could help reduce or amplify the resonance significantly.
ContributorsLe, Thao (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Brad (Committee member) / Arizona State University (Publisher)
Created2018
Description
Encapsulant is a key packaging component of photovoltaic (PV) modules, which protects the solar cell from physical, environmental and electrical damages. Ethylene-vinyl acetate (EVA) is one of the major encapsulant materials used in the PV industry. This work focuses on indoor accelerated ultraviolet (UV) stress testing and characterization to investigate the EVA discoloration and delamination in PV modules by using various non-destructive characterization techniques, including current-voltage (IV) measurements, UV fluorescence (UVf) and colorimetry measurements. Mini-modules with glass/EVA/cell/EVA/backsheet construction were fabricated in the laboratory with two types of EVA, UV-cut EVA (UVC) and UV-pass EVA (UVP).
The accelerated UV testing was performed in a UV chamber equipped with UV lights at an ambient temperature of 50°C, little or no humidity and total UV dosage of 400 kWh/m2. The mini-modules were maintained at three different temperatures through UV light heating by placing different thickness of thermal insulation sheets over the backsheet. Also, prior to thermal insulation sheet placement, the backsheet and laminate edges were fully covered with aluminum tape to prevent oxygen diffusion into the module and hence the photobleaching reaction.
The characterization results showed that mini-modules with UV-cut EVA suffered from discoloration while the modules with UV-pass EVA suffered from delamination. UVf imaging technique has the capability to identify the discoloration region in the UVC modules in the very early stage when the discoloration is not visible to the naked eyes, whereas Isc measurement is unable to measure the performance loss until the color becomes visibly darker. YI also provides the direct evidence of yellowing in the encapsulant. As expected, the extent of degradation due to discoloration increases with the increase in module temperature. The Isc loss is dictated by both the regions – discolored area at the center and non-discolored area at the cell edges, whereas the YI is only determined at the discolored region due to low probe area. This led to the limited correlation between Isc and YI in UVC modules.
In case of UVP modules, UV radiation has caused an adverse impact on the interfacial adhesion between the EVA and solar cell, which was detected from UVf images and severe Isc loss. No change in YI confirms that the reason for Isc loss is not due to yellowing but the delamination.
Further, the activation energy of encapsulant discoloration was estimated by using Arrhenius model on two types of data, %Isc drop and ΔYI. The Ea determined from the change in YI data for the EVA encapsulant discoloration reaction without the influence of oxygen and humidity is 0.61 eV. Based on the activation energy determined in this work and hourly weather data of any site, the degradation rate for the encaspulant browning mode can be estimated.
The accelerated UV testing was performed in a UV chamber equipped with UV lights at an ambient temperature of 50°C, little or no humidity and total UV dosage of 400 kWh/m2. The mini-modules were maintained at three different temperatures through UV light heating by placing different thickness of thermal insulation sheets over the backsheet. Also, prior to thermal insulation sheet placement, the backsheet and laminate edges were fully covered with aluminum tape to prevent oxygen diffusion into the module and hence the photobleaching reaction.
The characterization results showed that mini-modules with UV-cut EVA suffered from discoloration while the modules with UV-pass EVA suffered from delamination. UVf imaging technique has the capability to identify the discoloration region in the UVC modules in the very early stage when the discoloration is not visible to the naked eyes, whereas Isc measurement is unable to measure the performance loss until the color becomes visibly darker. YI also provides the direct evidence of yellowing in the encapsulant. As expected, the extent of degradation due to discoloration increases with the increase in module temperature. The Isc loss is dictated by both the regions – discolored area at the center and non-discolored area at the cell edges, whereas the YI is only determined at the discolored region due to low probe area. This led to the limited correlation between Isc and YI in UVC modules.
In case of UVP modules, UV radiation has caused an adverse impact on the interfacial adhesion between the EVA and solar cell, which was detected from UVf images and severe Isc loss. No change in YI confirms that the reason for Isc loss is not due to yellowing but the delamination.
Further, the activation energy of encapsulant discoloration was estimated by using Arrhenius model on two types of data, %Isc drop and ΔYI. The Ea determined from the change in YI data for the EVA encapsulant discoloration reaction without the influence of oxygen and humidity is 0.61 eV. Based on the activation energy determined in this work and hourly weather data of any site, the degradation rate for the encaspulant browning mode can be estimated.
ContributorsDolia, Kshitiz (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Green, Matthew (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2018