Matching Items (27)
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- All Subjects: engineering
- Creators: Sugar, Thomas
- Creators: Srinivasan, Devarajan
- Member of: ASU Electronic Theses and Dissertations
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
While the piezoelectric effect has been around for some time, it has only recently caught interest as a potential sustainable energy harvesting device. Piezoelectric energy harvesting has been developed for shoes and panels, but has yet to be integrated into a marketable bicycle tire. For this thesis, the development and feasibility of a piezoelectric tire was done. This includes the development of a circuit that incorporates piezoceramic elements, energy harvesting circuitry, and an energy storage device. A single phase circuit was designed using an ac-dc diode rectifier. An electrolytic capacitor was used as the energy storage device. A financial feasibility was also done to determine targets for manufacturing cost and sales price. These models take into account market trends for high performance tires, economies of scale, and the possibility of government subsidies. This research will help understand the potential for the marketability of a piezoelectric energy harvesting tire that can create electricity for remote use. This study found that there are many obstacles that must be addressed before a piezoelectric tire can be marketed to the general public. The power output of this device is miniscule compared to an alkaline battery. In order for this device to approach the power output of an alkaline battery the weight of the device would also become an issue. Additionally this device is very costly compared to the average bicycle tire. Lastly, this device is extreme fragile and easily broken. In order for this device to become marketable the issues of power output, cost, weight, and durability must all be successfully overcome.
ContributorsMalotte, Christopher (Author) / Madakannan, Arunachalanadar (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2012
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
Description
Global photovoltaic (PV) module installation in 2018 is estimated to exceed 100 GW, and crystalline Si (c-Si) solar cell-based modules have a share more than 90% of the global PV market. To reduce the social cost of PV electricity, further developments in reliability of solar panels are expected. These will lead to realize longer module lifetime and reduced levelized cost of energy. As many as 86 failure modes are observed in PV modules [1] and series resistance increase is one of the major durability issues of all. Series resistance constitutes emitter sheet resistance, metal-semiconductor contact resistance, and resistance across the metal-solder ribbon. Solder bond degradation at the cell interconnect is one of the primary causes for increase in series resistance, which is also considered to be an invisible defect [1]. Combination of intermetallic compounds (IMC) formation during soldering and their growth due to solid state diffusion over its lifetime result in formation of weak interfaces between the solar cell and the interconnect. Thermal cycling under regular operating conditions induce thermo-mechanical fatigue over these weak interfaces resulting in contact reduction or loss. Contact reduction or loss leads to increase in series resistance which further manifests into power and fill factor loss. The degree of intermixing of metallic interfaces and contact loss depends on climatic conditions as temperature and humidity (moisture ingression into the PV module laminate) play a vital role in reaction kinetics of these layers. Modules from Arizona and Florida served as a good sample set to analyze the effects of hot and humid climatic conditions respectively. The results obtained in the current thesis quantifies the thickness of IMC formation from SEM-EDS profiles, where similar modules obtained from different climatic conditions were compared. The results indicate the thickness of the IMC and detachment degree to be growing with age and operating temperatures of the module. This can be seen in CuxSny IMC which is thicker in the case of Arizona module. The results obtained from FL
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aged modules also show that humidity accelerates the formation of IMC as they showed thicker AgxSny layer and weak interconnect-contact interfaces as compared to Arizona modules. It is also shown that climatic conditions have different effects on rate at which CuxSny and AgxSny intermetallic compounds are formed.
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aged modules also show that humidity accelerates the formation of IMC as they showed thicker AgxSny layer and weak interconnect-contact interfaces as compared to Arizona modules. It is also shown that climatic conditions have different effects on rate at which CuxSny and AgxSny intermetallic compounds are formed.
ContributorsBuddha, Viswa Sai Pavan (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Alford, Terry (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2018
Description
A control method based on the phase angle is used to control oscillating systems. The phase oscillator uses the sine and cosine of the phase angle to change key properties of a mass-spring-damper system, including amplitude, frequency, and equilibrium. An inverted pendulum is used to show a further application of the phase oscillator. Two methods of control based on the phase oscillator are used for swing-up and balancing of the pendulum. The first control method involves two separate stages. The scenarios where this control works are discussed. The second control method uses variable coefficients to result in a smooth transition between swing-up and balancing.
ContributorsBates, Andrew (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2015
Description
The deposition of airborne dust, especially in desert conditions, is very problematic as it leads to significant loss of power of photovoltaic (PV) modules on a daily basis during the dry period. As such, PV testing laboratories around the world have been trying to set up soil deposition stations to artificially deposit soil layers and to simulate outdoor soiling conditions in an accelerated manner. This thesis is a part of a twin thesis. The first thesis, authored by Shanmukha Mantha, is associated with the designing of an artificial soiling station. The second thesis (this thesis), authored by Darshan Choudhary, is associated with the characterization of the deposited soil layers. The soil layers deposited on glass coupons and one-cell laminates are characterized and presented in this thesis. This thesis focuses on the characterizations of the soil layers obtained in several soiling cycles using various techniques including current-voltage (I-V), quantum efficiency (QE), compositional analysis and optical profilometry. The I-V characterization was carried out to determine the impact of soil layer on current and other performance parameters of PV devices. The QE characterization was carried out to determine the impact of wavelength dependent influence of soil type and thickness on the QE curves. The soil type was determined using the compositional analysis. The compositional data of the soil is critical to determine the adhesion properties of the soil layers on the surface of PV modules. The optical profilometry was obtained to determine the particle size and distribution. The soil layers deposited using two different deposition techniques were characterized. The two deposition techniques are designated as “dew” technique and “humidity” technique. For the same deposition time, the humidity method was determined to deposit the soil layer at lower rates as compared to the dew method. Two types of deposited soil layers were characterized. The first type layer was deposited using a reference soil called Arizona (AZ) dust. The second type layer was deposited using the soil which was collected from the surface of the modules installed outdoor in Arizona. The density of the layers deposited using the surface collected soil was determined to be lower than AZ dust based layers for the same number of deposition cycles.
ContributorsChoudhary, Darshan (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley Barney (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2016