Matching Items (41)
Description
Ethylene vinyl acetate (EVA) is the most commonly used encapsulant in photovoltaic modules. However, EVA degrades over time and causes performance losses in PV system. Therefore, EVA degradation is a matter of concern from a durability point of view.
This work compares EVA encapsulant degradation in glass/backsheet and glass/glass field-aged PV modules. EVA was extracted from three field-aged modules (two glass/backsheet and one glass/glass modules) from three different manufacturers from various regions (cell edges, cell centers, and non-cell region) from each module based on their visual and UV Fluorescence images. Characterization techniques such as I-V measurements, Colorimetry, Different Scanning Calorimetry, Thermogravimetric Analysis, Raman spectroscopy, and Fourier Transform Infrared Spectroscopy were performed on EVA samples.
The intensity of EVA discoloration was quantified using colorimetric measurements. Module performance parameters like Isc and Pmax degradation rates were calculated from I-V measurements. Properties such as degree of crystallinity, vinyl acetate content and degree of crosslinking were calculated from DSC, TGA, and Raman measurements, respectively. Polyenes responsible for EVA browning were identified in FTIR spectra.
The results from the characterization techniques confirmed that when EVA undergoes degradation, crosslinking in EVA increases beyond 90% causing a decrease in the degree of crystallinity and an increase in vinyl acetate content of EVA. Presence of polyenes in FTIR spectra of degraded EVA confirmed the occurrence of Norrish II reaction. However, photobleaching occurred in glass/backsheet modules due to the breathable backsheet whereas no photobleaching occurred in glass/glass modules because they were hermetically sealed. Hence, the yellowness index along with the Isc and Pmax degradation rates of EVA in glass/glass module is higher than that in glass/backsheet modules.
The results implied that more acetic acid was produced in the non-cell region due to its double layer of EVA compared to the front EVA from cell region. But, since glass/glass module is hermetically sealed, acetic acid gets entrapped inside the module further accelerating EVA degradation whereas it diffuses out through backsheet in glass/backsheet modules. Hence, it can be said that EVA might be a good encapsulant for glass/backsheet modules, but the same cannot be said for glass/glass modules.
This work compares EVA encapsulant degradation in glass/backsheet and glass/glass field-aged PV modules. EVA was extracted from three field-aged modules (two glass/backsheet and one glass/glass modules) from three different manufacturers from various regions (cell edges, cell centers, and non-cell region) from each module based on their visual and UV Fluorescence images. Characterization techniques such as I-V measurements, Colorimetry, Different Scanning Calorimetry, Thermogravimetric Analysis, Raman spectroscopy, and Fourier Transform Infrared Spectroscopy were performed on EVA samples.
The intensity of EVA discoloration was quantified using colorimetric measurements. Module performance parameters like Isc and Pmax degradation rates were calculated from I-V measurements. Properties such as degree of crystallinity, vinyl acetate content and degree of crosslinking were calculated from DSC, TGA, and Raman measurements, respectively. Polyenes responsible for EVA browning were identified in FTIR spectra.
The results from the characterization techniques confirmed that when EVA undergoes degradation, crosslinking in EVA increases beyond 90% causing a decrease in the degree of crystallinity and an increase in vinyl acetate content of EVA. Presence of polyenes in FTIR spectra of degraded EVA confirmed the occurrence of Norrish II reaction. However, photobleaching occurred in glass/backsheet modules due to the breathable backsheet whereas no photobleaching occurred in glass/glass modules because they were hermetically sealed. Hence, the yellowness index along with the Isc and Pmax degradation rates of EVA in glass/glass module is higher than that in glass/backsheet modules.
The results implied that more acetic acid was produced in the non-cell region due to its double layer of EVA compared to the front EVA from cell region. But, since glass/glass module is hermetically sealed, acetic acid gets entrapped inside the module further accelerating EVA degradation whereas it diffuses out through backsheet in glass/backsheet modules. Hence, it can be said that EVA might be a good encapsulant for glass/backsheet modules, but the same cannot be said for glass/glass modules.
ContributorsPatel, Aesha Parimalbhai (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Green, Matthew (Committee member) / Mu, Bin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Several decades of transistor technology scaling has brought the threat of soft errors to modern embedded processors. Several techniques have been proposed to protect these systems from soft errors. However, their effectiveness in protecting the computation cannot be ascertained without accurate and quantitative estimation of system reliability. Vulnerability -- a metric that defines the probability of system-failure (reliability) through analytical models -- is the most effective mechanism for our current estimation and early design space exploration needs. Previous vulnerability estimation tools are based around the Sim-Alpha simulator which has been to shown to have several limitations. In this thesis, I present gemV: an accurate and comprehensive vulnerability estimation tool based on gem5. Gem5 is a popular cycle-accurate micro-architectural simulator that can model several different processor models in close to real hardware form. GemV can be used for fast and early design space exploration and also evaluate the protection afforded by commodity processors. gemV is comprehensive, since it models almost all sequential components of the processor. gemV is accurate because of fine-grain vulnerability tracking, accurate vulnerability modeling of squashed instructions, and accurate vulnerability modeling of shared data structures in gem5. gemV has been thoroughly validated against extensive fault injection experiments and achieves a 97\% accuracy with 95\% confidence. A micro-architect can use gemV to discover micro-architectural variants of a processor that minimize vulnerability for allowed performance penalty. A software developer can use gemV to explore the performance-vulnerability trade-off by choosing different algorithms and compiler optimizations, while the system designer can use gemV to explore the performance-vulnerability trade-offs of choosing different Insruction Set Architectures (ISA).
ContributorsTanikella, Srinivas Karthik (Author) / Shrivastava, Aviral (Thesis advisor) / Bazzi, Rida (Committee member) / Wu, Carole-Jean (Committee member) / Arizona State University (Publisher)
Created2016
Description
The fatigue resistance of asphalt concrete (AC) plays an important role in the service life of a pavement. For predicting the fatigue life of AC, there are several existing empirical and mechanistic models. However, the assessment and quantification of the ‘reliability’ of the predictions from these models is a substantial knowledge gap. The importance of reliability in AC material performance predictions becomes all the more important in light of limited monetary and material resources. The goal of this dissertation research is to address these shortcomings by developing a framework for incorporating reliability into the prediction of mechanical models for AC and to improve the reliability of AC material performance prediction by using Fine Aggregate Matrix (FAM) phase data. The goal of the study is divided into four objectives; 1) development of a reliability framework for fatigue life prediction of AC materials using the simplified viscoelastic continuum damage (S-VECD) model, 2) development of test protocols for FAM in similar loading conditions as AC, 3) evaluation of the mechanical linkages between the AC and FAM mix through upscaling analysis, and 4) investigation of the hypothesis that the reliability of fatigue life prediction of AC can be improved with FAM data modeling.
In this research effort, a reliability framework is developed using Monte Carlo simulation for predicting the fatigue life of AC material using the S-VECD model. The reliability analysis reveals that the fatigue life prediction is very sensitive to the uncertainty in the input variables. FAM testing in similar loading conditions as AC, and upscaling of AC modulus and damage response using FAM properties from a relatively simple homogenized continuum approach shows promising results. The FAM phase fatigue life prediction and upscaling of FAM results to AC show more reliable fatigue life prediction than the fatigue life prediction of AC material using its experimental data. To assess the sensitivity of fatigue life prediction model to uncertainty in the input variables, a parametric sensitivity study is conducted on the S-VECD model. Overall, the findings from this research show promising results both in terms of upscaling FAM to AC properties and the reliability of fatigue prediction in AC using experimental data on FAM.
In this research effort, a reliability framework is developed using Monte Carlo simulation for predicting the fatigue life of AC material using the S-VECD model. The reliability analysis reveals that the fatigue life prediction is very sensitive to the uncertainty in the input variables. FAM testing in similar loading conditions as AC, and upscaling of AC modulus and damage response using FAM properties from a relatively simple homogenized continuum approach shows promising results. The FAM phase fatigue life prediction and upscaling of FAM results to AC show more reliable fatigue life prediction than the fatigue life prediction of AC material using its experimental data. To assess the sensitivity of fatigue life prediction model to uncertainty in the input variables, a parametric sensitivity study is conducted on the S-VECD model. Overall, the findings from this research show promising results both in terms of upscaling FAM to AC properties and the reliability of fatigue prediction in AC using experimental data on FAM.
ContributorsGudipudi, Padmini Priyadarsini (Author) / Underwood, Benjamin S (Thesis advisor) / Kaloush, Kamil (Committee member) / Mamlouk, Michael (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2016
Description
Soft errors are considered as a key reliability challenge for sub-nano scale transistors. An ideal solution for such a challenge should ultimately eliminate the effect of soft errors from the microprocessor. While forward recovery techniques achieve fast recovery from errors by simply voting out the wrong values, they incur the overhead of three copies execution. Backward recovery techniques only need two copies of execution, but suffer from check-pointing overhead.
In this work I explored the efficiency of integrating check-pointing into the application and the effectiveness of recovery that can be performed upon it. After evaluating the available fine-grained approaches to perform recovery, I am introducing InCheck, an in-application recovery scheme that can be integrated into instruction-duplication based techniques, thus providing a fast error recovery. The proposed technique makes light-weight checkpoints at the basic-block granularity, and uses them for recovery purposes.
To evaluate the effectiveness of the proposed technique, 10,000 fault injection experiments were performed on different hardware components of a modern ARM in-order simulated processor. InCheck was able to recover from all detected errors by replaying about 20 instructions, however, the state of the art recovery scheme failed more than 200 times.
In this work I explored the efficiency of integrating check-pointing into the application and the effectiveness of recovery that can be performed upon it. After evaluating the available fine-grained approaches to perform recovery, I am introducing InCheck, an in-application recovery scheme that can be integrated into instruction-duplication based techniques, thus providing a fast error recovery. The proposed technique makes light-weight checkpoints at the basic-block granularity, and uses them for recovery purposes.
To evaluate the effectiveness of the proposed technique, 10,000 fault injection experiments were performed on different hardware components of a modern ARM in-order simulated processor. InCheck was able to recover from all detected errors by replaying about 20 instructions, however, the state of the art recovery scheme failed more than 200 times.
ContributorsLokam, Sai Ram Dheeraj (Author) / Shrivastava, Aviral (Thesis advisor) / Clark, Lawrence T (Committee member) / Mubayi, Anuj (Committee member) / Arizona State University (Publisher)
Created2016
Description
Potential-Induced Degradation (PID) is an extremely serious photovoltaic (PV) durability issue significantly observed in crystalline silicon PV modules due to its rapid power degradation, particularly when compared to other PV degradation modes. The focus of this dissertation is to understand PID mechanisms and to develop PID-free cells and modules. PID-affected modules have been claimed to be fully recovered by high temperature and reverse potential treatments. However, the results obtained in this work indicate that the near-full recovery of efficiency can be achieved only at high irradiance conditions, but the full recovery of efficiency at low irradiance levels, of shunt resistance, and of quantum efficiency (QE) at short wavelengths could not be achieved. The QE loss observed at short wavelengths was modeled by changing the front surface recombination velocity. The QE scaling error due to a measurement on a PID shunted cell was addressed by developing a very low input impedance accessory applicable to an existing QE system. The impacts of silicon nitride (SiNx) anti-reflection coating (ARC) refractive index (RI) and emitter sheet resistance on PID are presented. Low RI ARC cells (1.87) were observed to be PID-susceptible whereas high RI ARC cells (2.05) were determined to be PID-resistant using a method employing high dose corona charging followed by time-resolved measurement of surface voltage. It has been demonstrated that the PID could be prevented by deploying an emitter having a low sheet resistance (~ 60 /sq) even if a PID-susceptible ARC is used in a cell. Secondary ion mass spectroscopy (SIMS) results suggest that a high phosphorous emitter layer hinders sodium transport, which is responsible for the PID. Cells can be screened for PID susceptibility by illuminated lock-in thermography (ILIT) during the cell fabrication process, and the sample structure for this can advantageously be simplified as long as the sample has the SiNx ARC and an aluminum back surface field. Finally, this dissertation presents a prospective method for eliminating or minimizing the PID issue either in the factory during manufacturing or in the field after system installation. The method uses commercially available, thin, and flexible Corning® Willow® Glass sheets or strips on the PV module glass superstrates, disrupting the current leakage path from the cells to the grounded frame.
ContributorsOh, Jaewon (Author) / Bowden, Stuart (Thesis advisor) / Tamizhmani, Govindasamy (Thesis advisor) / Honsberg, Christiana (Committee member) / Hacke, Peter (Committee member) / Schroder, Dieter (Committee member) / Arizona State University (Publisher)
Created2016
Description
This is a two-part thesis assessing the long-term reliability of photovoltaic modules.
Part 1: Manufacturing dependent reliability - Adapting FMECA for quality control in PV module manufacturing
This part is aimed at introducing a statistical tool in quality assessments in PV module manufacturing. Developed jointly by ASU-PRL and Clean Energy Associates, this work adapts the Failure Mode Effect and Criticality Analysis (FMECA, IEC 60812) to quantify the impact of failure modes observed at the time of manufacturing. The method was developed through analysis of nearly 9000 modules at the pre-shipment evaluation stage in module manufacturing facilities across south east Asia. Numerous projects were analyzed to generate RPN (Risk Priority Number) scores for projects. In this manner, it was possibly to quantitatively assess the risk being carried the project at the time of shipment of modules. The objective of this work was to develop a benchmarking system that would allow for accurate quantitative estimations of risk mitigation and project bankability.
Part 2: Climate dependent reliability - Activation energy determination for climate specific degradation modes
This work attempts to model the parameter (Isc or Rs) degradation rate of modules as a function of the climatic parameters (i.e. temperature, relative humidity and ultraviolet radiation) at the site. The objective of this work was to look beyond the power degradation rate and model based on the performance parameter directly affected by the degradation mode under investigation (encapsulant browning or IMS degradation of solder bonds). Different physical models were tested and validated through comparing the activation energy obtained for each degradation mode. It was concluded that, for the degradation of the solder bonds within the module, the Pecks equation (function of temperature and relative humidity) modelled with Rs increase was the best fit; the activation energy ranging from 0.4 – 0.7 eV based on the climate type. For encapsulant browning, the Modified Arrhenius equation (function of temperature and UV) seemed to be the best fit presently, yielding an activation energy of 0.3 eV. The work was concluded by suggesting possible modifications to the models based on degradation pathways unaccounted for in the present work.
Part 1: Manufacturing dependent reliability - Adapting FMECA for quality control in PV module manufacturing
This part is aimed at introducing a statistical tool in quality assessments in PV module manufacturing. Developed jointly by ASU-PRL and Clean Energy Associates, this work adapts the Failure Mode Effect and Criticality Analysis (FMECA, IEC 60812) to quantify the impact of failure modes observed at the time of manufacturing. The method was developed through analysis of nearly 9000 modules at the pre-shipment evaluation stage in module manufacturing facilities across south east Asia. Numerous projects were analyzed to generate RPN (Risk Priority Number) scores for projects. In this manner, it was possibly to quantitatively assess the risk being carried the project at the time of shipment of modules. The objective of this work was to develop a benchmarking system that would allow for accurate quantitative estimations of risk mitigation and project bankability.
Part 2: Climate dependent reliability - Activation energy determination for climate specific degradation modes
This work attempts to model the parameter (Isc or Rs) degradation rate of modules as a function of the climatic parameters (i.e. temperature, relative humidity and ultraviolet radiation) at the site. The objective of this work was to look beyond the power degradation rate and model based on the performance parameter directly affected by the degradation mode under investigation (encapsulant browning or IMS degradation of solder bonds). Different physical models were tested and validated through comparing the activation energy obtained for each degradation mode. It was concluded that, for the degradation of the solder bonds within the module, the Pecks equation (function of temperature and relative humidity) modelled with Rs increase was the best fit; the activation energy ranging from 0.4 – 0.7 eV based on the climate type. For encapsulant browning, the Modified Arrhenius equation (function of temperature and UV) seemed to be the best fit presently, yielding an activation energy of 0.3 eV. The work was concluded by suggesting possible modifications to the models based on degradation pathways unaccounted for in the present work.
ContributorsPore, Shantanu (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Green, Matthew (Thesis advisor) / Srinivasan, Devrajan (Committee member) / Arizona State University (Publisher)
Created2017
Description
Distributed Renewable energy generators are now contributing a significant amount of energy into the energy grid. Consequently, reliability adequacy of such energy generators will depend on making accurate forecasts of energy produced by them. Power outputs of Solar PV systems depend on the stochastic variation of environmental factors (solar irradiance, ambient temperature & wind speed) and random mechanical failures/repairs. Monte Carlo Simulation which is typically used to model such problems becomes too computationally intensive leading to simplifying state-space assumptions. Multi-state models for power system reliability offer a higher flexibility in providing a description of system state evolution and an accurate representation of probability. In this study, Universal Generating Functions (UGF) were used to solve such combinatorial problems. 8 grid connected Solar PV systems were analyzed with a combined capacity of about 5MW located in a hot-dry climate (Arizona) and accuracy of 98% was achieved when validated with real-time data. An analytics framework is provided to grid operators and utilities to effectively forecast energy produced by distributed energy assets and in turn, develop strategies for effective Demand Response in times of increased share of renewable distributed energy assets in the grid. Second part of this thesis extends the environmental modelling approach to develop an aging test to be run in conjunction with an accelerated test of Solar PV modules. Accelerated Lifetime Testing procedures in the industry are used to determine the dominant failure modes which the product undergoes in the field, as well as predict the lifetime of the product. UV stressor is one of the ten stressors which a PV module undergoes in the field. UV exposure causes browning of modules leading to drop in Short Circuit Current. This thesis presents an environmental modelling approach for the hot-dry climate and extends it to develop an aging test methodology. This along with the accelerated tests would help achieve the goal of correlating field failures with accelerated tests and obtain acceleration factor. This knowledge would help predict PV module degradation in the field within 30% of the actual value and help in knowing the PV module lifetime accurately.
ContributorsKadloor, Nikhil (Author) / Kuitche, Joseph (Thesis advisor) / Pan, Rong (Thesis advisor) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2017
Description
Hydropower generation is one of the clean renewable energies which has received great attention in the power industry. Hydropower has been the leading source of renewable energy. It provides more than 86% of all electricity generated by renewable sources worldwide. Generally, the life span of a hydropower plant is considered as 30 to 50 years. Power plants over 30 years old usually conduct a feasibility study of rehabilitation on their entire facilities including infrastructure. By age 35, the forced outage rate increases by 10 percentage points compared to the previous year. Much longer outages occur in power plants older than 20 years. Consequently, the forced outage rate increases exponentially due to these longer outages. Although these long forced outages are not frequent, their impact is immense. If reasonable timing of rehabilitation is missed, an abrupt long-term outage could occur and additional unnecessary repairs and inefficiencies would follow. On the contrary, too early replacement might cause the waste of revenue. The hydropower plants of Korea Water Resources Corporation (hereafter K-water) are utilized for this study. Twenty-four K-water generators comprise the population for quantifying the reliability of each equipment. A facility in a hydropower plant is a repairable system because most failures can be fixed without replacing the entire facility. The fault data of each power plant are collected, within which only forced outage faults are considered as raw data for reliability analyses. The mean cumulative repair functions (MCF) of each facility are determined with the failure data tables, using Nelson's graph method. The power law model, a popular model for a repairable system, can also be obtained to represent representative equipment and system availability. The criterion-based analysis of HydroAmp is used to provide more accurate reliability of each power plant. Two case studies are presented to enhance the understanding of the availability of each power plant and represent economic evaluations for modernization. Also, equipment in a hydropower plant is categorized into two groups based on their reliability for determining modernization timing and their suitable replacement periods are obtained using simulation.
ContributorsKwon, Ogeuk (Author) / Holbert, Keith E. (Thesis advisor) / Heydt, Gerald T (Committee member) / Pan, Rong (Committee member) / Arizona State University (Publisher)
Created2011
Description
Neural networks are increasingly becoming attractive solutions for automated systems within automotive, aerospace, and military industries.Since many applications in such fields are both real-time and safety-critical, strict performance and reliability constraints must be considered. To achieve high performance, specialized architectures are required.Given that over 90% of the workload in modern neural network topologies is dominated by matrix multiplication, accelerating said algorithm becomes of paramount importance.
Modern neural network accelerators, such as Xilinx's Deep Processing Unit (DPU), adopt efficient systolic-like architectures. Thanks to their high degree of parallelism and design flexibility, Field-Programmable Gate Arrays (FPGAs) are among the most promising devices for speeding up matrix multiplication and neural network computation.However, SRAM-based FPGAs are also known to suffer from radiation-induced upsets in their configuration memories. To achieve high reliability, hardening strategies must be put in place.However, traditional modular redundancy of inherently expensive modules is not always feasible due to limited resource availability on target devices.
Therefore, more efficient and cleverly designed hardening methods become a necessity.
For instance, Algorithm-Based Fault-Tolerance (ABFT) exploits algorithm characteristics to deliver error detection/correction capabilities at significantly lower costs. First, experimental results with Xilinx's DPU indicate that failure rates can be over twice as high as the limits specified for terrestrial applications.In other words, the undeniable need for hardening in the state-of-the-art neural network accelerator for FPGAs is demonstrated. Later, an extensive multi-level fault propagation analysis is presented, and an ultra-low-cost algorithm-based error detection strategy for matrix multiplication is proposed.By considering the specifics of FPGAs' fault model, this novel hardening method decreases costs of implementation by over a polynomial degree, when compared to state-of-the-art solutions.
A corresponding architectural implementation is suggested, incurring area and energy overheads lower than 1% for the vast majority of systolic arrays dimensions. Finally, the impact of fundamental design decisions, such as data precision in processing elements, and overall degree of parallelism, on the reliability of hypothetical neural network accelerators is experimentally investigated.A novel way of predicting the compound failure rate of inherently inaccurate algorithms/applications in the presence of radiation is also provided.
ContributorsLibano, Fabiano (Author) / Brunhaver, John (Thesis advisor) / Clark, Lawrence (Committee member) / Quinn, Heather (Committee member) / Rech, Paolo (Committee member) / Arizona State University (Publisher)
Created2021
Description
In-field characterization of photovoltaics is crucial to understanding performance and degradation mechanisms, subsequently improving overall reliability and lifespans. Current outdoor characterization is often limited by logistical difficulties, variable weather, and requirements to measure during peak production hours. It becomes a challenge to find a characterization technique that is affordable with a low impact on system performance while still providing useful device parameters. For added complexity, this characterization technique must have the ability to scale for implementation in large powerplant applications. This dissertation addresses some of the challenges of outdoor characterization by expanding the knowledge of a well-known indoor technique referred to as Suns-VOC. Suns-VOC provides a pseudo current-voltage curve that is free of any effects from series resistance. Device parameters can be extracted from this pseudo I-V curve, allowing for subsequent degradation analysis. This work introduces how to use Suns-VOC outdoors while normalizing results based on the different effects of environmental conditions. This technique is validated on single-cells, modules, and small arrays with accuracies capable of measuring yearly degradation. An adaptation to Suns-VOC, referred to as Suns-Voltage-Resistor (Suns-VR), is also introduced to complement the results from Suns-VOC. This work can potentially be used to provide a diagnostic tool for outdoor characterization in various applications, including residential, commercial, and industrial PV systems.
ContributorsKillam, Alexander Cameron (Author) / Bowden, Stuart G (Thesis advisor) / Goryll, Michael (Committee member) / Augusto, Andre (Committee member) / Rand, James (Committee member) / Arizona State University (Publisher)
Created2022