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- All Subjects: engineering
- All Subjects: Photovoltaic
- Creators: Rogers, Bradley
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
ABSTRACT As the use of photovoltaic (PV) modules in large power plants continues to increase globally, more studies on degradation, reliability, failure modes, and mechanisms of field aged modules are needed to predict module life expectancy based on accelerated lifetime testing of PV modules. In this work, a 26+ year old PV power plant in Phoenix, Arizona has been evaluated for performance, reliability, and durability. The PV power plant, called Solar One, is owned and operated by John F. Long's homeowners association. It is a 200 kWdc, standard test conditions (STC) rated power plant comprised of 4000 PV modules or frameless laminates, in 100 panel groups (rated at 175 kWac). The power plant is made of two center-tapped bipolar 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 south array and the other thesis presents the results obtained on the north array. Each of these two arrays is made of four sub arrays, the east sub arrays (positive and negative polarities) and the west sub arrays (positive and negative polarities), making up eight sub arrays. The evaluation and analyses of the power plant included in this thesis consists of: visual inspection, electrical performance measurements, and infrared thermography. A possible presence of potential induced degradation (PID) due to potential difference between ground and strings was also investigated. Some installation practices were also studied and found to contribute to the power loss observed in this investigation. The power output measured in 2011 for all eight sub arrays at STC is approximately 76 kWdc and represents a power loss of 62% (from 200 kW to 76 kW) over 26+ years. The 2011 measured power output for the four south sub arrays at STC is 39 kWdc and represents a power loss of 61% (from 100 kW to 39 kW) over 26+ years. Encapsulation browning and non-cell interconnect ribbon breakages were determined to be the primary causes for the power loss.
ContributorsOlakonu, Kolapo (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
This is a two-part thesis: Part 1 of this thesis tests and validates the methodology and mathematical models of the International Electrotechnical Commission (IEC) 61853-2 standard for the measurement of angle of incidence (AOI) effects on photovoltaic modules. Flat-plate photovoltaic modules in the field operate under a wide range of environmental conditions. The purpose of IEC 61853-2 is to characterize photovoltaic modules' performance under specific environmental conditions. Part 1 of this report focuses specifically on AOI. To accurately test and validate IEC 61853-2 standard for measuring AOI, meticulous experimental setup and test procedures were followed. Modules of five different photovoltaic technology types with glass superstrates were tested. Test results show practically identical relative light transmission plots for all five test modules. The experimental results were compared to theoretical and empirical models for relative light transmission of air-glass interface. IEC 61853-2 states "for the flat glass superstrate modules, the AOI test does not need to be performed; rather, the data of a flat glass air interface can be used." The results obtained in this thesis validate this statement. This work was performed in collaboration with another Master of Science student (Surynarayana Janakeeraman) and the test results are presented in two masters theses. Part 2 of this thesis is to develop non-intrusive techniques to accurately measure the quantum efficiency (QE) of a single-junction crystalline silicon cell within a commercial module. This thesis will describe in detail all the equipment and conditions necessary to measure QE and discuss the factors which may influence this measurement. The ability to utilize a non-intrusive test to measure quantum efficiency of a cell within a module is extremely beneficial for reliability testing of commercial modules. Detailed methodologies for this innovative test procedure are not widely available in industry because equipment and measurement techniques have not been explored extensively. This paper will provide a literature review describing relevant theories and measurement techniques related to measuring the QE of a cell within a module. The testing methodology and necessary equipment will be described in detail. Results and conclusions provide the overall accuracy of the measurements and discuss the parameters affecting these measurements.
ContributorsKnisely, Brett (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Macia, Narciso (Committee member) / Arizona State University (Publisher)
Created2013
Description
Today's energy market is facing large-scale changes that will affect all market players. Near the top of that list is the rapid deployment of residential solar photovoltaic (PV) systems. Yet that growing trend will be influenced multiple competing interests between various stakeholders, namely the utility, consumers and technology provides. This study provides a series of analyses--utility-side, consumer-side, and combined analyses--to understand and evaluate the effect of increases in residential solar PV market
penetration. Three urban regions have been selected as study locations--Chicago, Phoenix, Seattle--with simulated load data and solar insolation data at each locality. Various time-of-use pricing schedules are investigated, and the effect of net metering is evaluated to determine the optimal capacity of solar PV and battery storage in a typical residential home. The net residential load profile is scaled to assess system-wide technical and economic figures of merit for the utility with an emphasis on intraday load profiles, ramp rates and electricity sales with increasing solar PV penetration. The combined analysis evaluates the least-cost solar PV system for the consumer and models the associated system-wide effects on the electric grid. Utility revenue was found to drop by 1.2% for every percent PV penetration increase, net metering on a monthly or annual basis improved the cost-effectiveness of solar PV but not battery storage, the removal of net metering policy and usage of an improved the cost-effectiveness of battery storage and increases in solar PV penetration reduced the system load factor. As expected, Phoenix had the most favorable economic scenario for residential solar PV, primarily due to high solar insolation. The study location--solar insolation and load profile--was also found to affect the time of
year at which the largest net negative system load was realized.
penetration. Three urban regions have been selected as study locations--Chicago, Phoenix, Seattle--with simulated load data and solar insolation data at each locality. Various time-of-use pricing schedules are investigated, and the effect of net metering is evaluated to determine the optimal capacity of solar PV and battery storage in a typical residential home. The net residential load profile is scaled to assess system-wide technical and economic figures of merit for the utility with an emphasis on intraday load profiles, ramp rates and electricity sales with increasing solar PV penetration. The combined analysis evaluates the least-cost solar PV system for the consumer and models the associated system-wide effects on the electric grid. Utility revenue was found to drop by 1.2% for every percent PV penetration increase, net metering on a monthly or annual basis improved the cost-effectiveness of solar PV but not battery storage, the removal of net metering policy and usage of an improved the cost-effectiveness of battery storage and increases in solar PV penetration reduced the system load factor. As expected, Phoenix had the most favorable economic scenario for residential solar PV, primarily due to high solar insolation. The study location--solar insolation and load profile--was also found to affect the time of
year at which the largest net negative system load was realized.
ContributorsArnold, Michael (Author) / Johnson, Nathan G (Thesis advisor) / Rogers, Bradley (Committee member) / Ruddell, Benjamin (Committee member) / Arizona State University (Publisher)
Created2014
Description
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.
ContributorsSupriti, Shahina Parvin (Author) / Rogers, Bradley (Thesis advisor) / Madakannan, Arunachalanadar (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2015
Description
This report presents the effects and analysis of the effects of Pulsed-Gas Metal Arc Welding's (P-GMAW) on Lean Duplex stainless steel. Although the welding of Duplex and Super Duplex Stainless steels have been well documented in both the laboratory and construction industry, the use of Lean Duplex has not. The purpose for conducting this research is to ensure that the correct Ferrite-Austenite phase balance along with the correct welding procedures are used in the creation of reactor cores for new construction nuclear power generation stations. In this project the effects of Lincoln Electrics ER-2209 GMAW wire are studied. Suggestions and improvements to the welding process are then proposed in order to increase the weldability, strength, gas selection, and ferrite count. The weldability will be measured using X-Ray photography in order to determine if any inclusions, lack of fusion, or voids are found post welding, along with welder feedback. The ferritic point count method in accordance with ASTM A562-08, is employed so that the amount of ferrite and austenite can be calculated in the same manor that is currently being used in industry. These will then be correlated to the tensile strength and impact toughness in the heat-affected zone (HAZ) of the weld based on the ASTM A923 testing method.
ContributorsCarter, Roger (Author) / Rogers, Bradley (Thesis advisor) / Gintz, Jerry (Committee member) / Georgeou, Trian (Committee member) / Arizona State University (Publisher)
Created2012
Description
Photovoltaic (PV) modules undergo performance degradation depending on climatic conditions, applications, and system configurations. The performance degradation prediction of PV modules is primarily based on Accelerated Life Testing (ALT) procedures. In order to further strengthen the ALT process, additional investigation of the power degradation of field aged PV modules in various configurations is required. A detailed investigation of 1,900 field aged (12-18 years) PV modules deployed in a power plant application was conducted for this study. Analysis was based on the current-voltage (I-V) measurement of all the 1,900 modules individually. I-V curve data of individual modules formed the basis for calculating the performance degradation of the modules. The percentage performance degradation and rates of degradation were compared to an earlier study done at the same plant. The current research was primarily focused on identifying the extent of potential induced degradation (PID) of individual modules with reference to the negative ground potential. To investigate this, the arrangement and connection of the individual modules/strings was examined in detail. The study also examined the extent of underperformance of every series string due to performance mismatch of individual modules in that string. The power loss due to individual module degradation and module mismatch at string level was then compared to the rated value.
ContributorsJaspreet Singh (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (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