339 results on '"Roger H. French"'
Search Results
2. Field studies of PERC and Al-BSF PV module performance loss using power and I-V timeseries
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Alan J. Curran, Xuanji Yu, Jiqi Liu, Dylan J. Colvin, Nafis Iqbal, Thomas Moran, Brent Brownell, Mengjie Li, Kristopher O. Davis, Bryan D. Huey, Jean-Nicolas Jaubert, Jennifer L. Braid, Laura S. Bruckman, and Roger H. French
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field studies ,PV module technology ,performance assessment ,I-V ,timeseries data ,Al-BSF ,General Works - Abstract
We have studied the degradation of both full-sized modules and minimodules with PERC and Al-BSF cell variations in fields while considering packaging strategies. We demonstrate the implementations of data-driven tools to analyze large numbers of modules and volumes of timeseries data to obtain the performance loss and degradation pathways. This data analysis pipeline enables quantitative comparison and ranking of module variations, as well as mapping and deeper understanding of degradation mechanisms. The best performing module is a half-cell PERC, which shows a performance loss rate (PLR) of −0.27 ± 0.12% per annum (%/a) after initial losses have stabilized. Minimodule studies showed inconsistent performance rankings due to significant power loss contributions via series resistance, however, recombination losses remained stable. Overall, PERC cell variations outperform or are not distinguishable from Al-BSF cell variations.
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- 2023
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3. Statistical analysis and degradation pathway modeling of photovoltaic minimodules with varied packaging strategies
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Sameera Nalin Venkat, Xuanji Yu, Jiqi Liu, Jakob Wegmueller, Jayvic Cristian Jimenez, Erika I. Barcelos, Hein Htet Aung, Xinjun Li, Jean-Nicolas Jaubert, Roger H. French, and Laura S. Bruckman
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degradation ,photovoltaics ,pathway modeling ,network structural equation modeling ,electrical measurements ,power loss ,General Works - Abstract
Degradation pathway models constructed using network structural equation modeling (netSEM) are used to study degradation modes and pathways active in photovoltaic (PV) system variants in exposure conditions of high humidity and temperature. This data-driven modeling technique enables the exploration of simultaneous pairwise and multiple regression relationships between variables in which several degradation modes are active in specific variants and exposure conditions. Durable and degrading variants are identified from the netSEM degradation mechanisms and pathways, along with potential ways to mitigate these pathways. A combination of domain knowledge and netSEM modeling shows that corrosion is the primary cause of the power loss in these glass/backsheet PV minimodules. We show successful implementation of netSEM to elucidate the relationships between variables in PV systems and predict a specific service lifetime. The results from pairwise relationships and multiple regression show consistency. This work presents a greater opportunity to be expanded to other materials systems.
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- 2023
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4. Surface Antireflection and Light Extraction Properties of GaN Microdomes
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Lu Han, Roger H. French, and Hongping Zhao
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Light emitting diodes ,Light-matter interactions ,Photovoltaics ,Photonic materials ,Applied optics. Photonics ,TA1501-1820 ,Optics. Light ,QC350-467 - Abstract
GaN microdomes were fabricated and measured as both an antireflection surface and a light extraction enhancement structure. The combination of self-assembled micro/nanosphere lithography and reactive ion etching process was used to fabricate GaN microdomes with different aspect ratios. SiO2 microspheres with diameters of 1000 and 500 nm, deposited on top of the GaN substrate using a dip-coating method, serve as the mask for the formation of GaN microdomes. The GaN microdome shapes and sizes were determined through control of the plasma etching conditions. The antireflection properties of the GaN microdomes with different sizes and shapes were characterized. Two different mechanisms were proposed to explain the surface reflection properties of incidence wavelength below and above GaN band gap, respectively. The trend shows that the surface reflection is reduced with the increase in the aspect ratio of the GaN microdomes for incidence wavelength above the band gap. For incidence wavelength below the band gap, the trend is totally different. Studies indicate that the microdomes are applicable not only as antireflection structures in solar cells but for enhancing light extraction in light-emitting diodes as well.
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- 2015
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5. Statistical and Domain Analytics Applied to PV Module Lifetime and Degradation Science
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Laura S. Bruckman, Nicholas R. Wheeler, Junheng Ma, Ethan Wang, Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French
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Photovoltaics ,statistical analytics ,lifetime and degradation science ,structural equation modeling ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
A better understanding of the degradation modes and rates for photovoltaic (PV) modules is necessary to optimize and extend the lifetime of these modules. Lifetime and degradation science (L&DS) is used to understand degradation modes, mechanisms and rates of materials, components and systems to predict lifetime of PV modules. A PV module lifetime and degradation science (PVM L&DS) model is an essential component to predict lifetime and mitigate degradation of PV modules using reproducible open data science. Previously published accelerated testing data from Underwriter Laboratories on PV modules with fluorinated polyester backsheets, which included eight modules that were exposed up to 4000 hrs of damp heat (85% relative humidity at 85°C) and eight exposed up to 4000 hrs of ultraviolet light (80 W/m2 of 280-400 nm wavelengths at 60°C) (UV preconditioning) were used to determine statistically significant relationships between the applied stresses and measured responses. There were 15 different variables tracking aspects of system performance, degradation mechanisms, component metrics and time. Modules were analyzed for three system performance metrics (fill factor, peak power, and wet insulation). The results were statistically analyzed to identify variable transformations, statistically significant relationships (SSRs) and to develop the PVM L&DS model informed by a generalization of structural equation modeling techniques. The SSRs and significant model coefficients, combined with domain analytics, incorporating materials science, chemistry, and physics expertise, produced a pathway diagram ranking the variables' impact on the system performance, which were iteratively examined using sound statistical analysis and diagnostics. The SSRs determined from the damp heat exposure for the system response of Pmax corresponded to the degradation pathway of polyester terephthalate (PET) and ethylene vinyl acetate (EVA) hydrolysis. A linear change point for the damp heat exposure with the system response of Pmax was determined to be 1890 hrs. The UV preconditioning exposure did not induce sufficient degradation shown by the quality of the R2 values for many of the best fitting models. This exemplifies the development of a methodology to determine rank ordered lifetime and degradation pathways present in modules and their effects on module performance over lifetime.
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- 2013
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6. Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic Degradation Analysis at Scale.
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Yangxin Fan, Raymond Wieser, Laura S. Bruckman, Roger H. French, and Yinghui Wu
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- 2024
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7. Enhancing Scientific Image Classification through Multimodal Learning: Insights from Chest X-Ray and Atomic Force Microscopy Datasets.
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David C. Meshnick, Nahal Shahini, Debargha Ganguly, Yinghui Wu, Roger H. French, and Vipin Chaudhary
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- 2023
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8. Accelerating Time to Science using CRADLE: A Framework for Materials Data Science.
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Arafath Nihar, Thomas G. Ciardi, Rounak Chawla, Olatunde Akanbi, Vipin Chaudhary, Yinghui Wu, and Roger H. French
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- 2023
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9. Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Data Imputation.
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Yangxin Fan, Xuanji Yu, Raymond Wieser, David Meakin, Avishai Shaton, Jean-Nicolas Jaubert, Robert Flottemesch, Michael Howell, Jennifer Braid, Laura S. Bruckman, Roger H. French, and Yinghui Wu
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- 2023
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10. System-Auditing, Data Analysis and Characteristics of Cyber Attacks for Big Data Systems.
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Liangyi Huang, Sophia Hall, Fei Shao, Arafath Nihar, Vipin Chaudhary, Yinghui Wu, Roger H. French, and Xusheng Xiao
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- 2022
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11. Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Timeseries Data Imputation.
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Yangxin Fan, Xuanji Yu, Raymond Wieser, David Meakin, Avishai Shaton, Jean-Nicolas Jaubert, Robert Flottemesch, Michael Howell, Jennifer Braid, Laura S. Bruckman, Roger H. French, and Yinghui Wu
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- 2023
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12. Spatiotemporal Graph Neural Network for Performance Prediction of Photovoltaic Power Systems.
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Ahmad Maroof Karimi, Yinghui Wu, Mehmet Koyutürk, and Roger H. French
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- 2021
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13. Materials data analytics for 9% Cr family steel.
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Vyacheslav N. Romanov, Narayanan Krishnamurthy, Amit K. Verma, Laura S. Bruckman, Roger H. French, Jennifer L. W. Carter, and Jeffrey A. Hawk
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- 2019
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14. Multiscale Characterization of Photovoltaic Modules—Case Studies of Contact and Interconnect Degradation
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Jean-Nicolas Jaubert, Dylan J. Colvin, Parag Banerjee, Bryan D. Huey, Alan J. Curran, Andrew G. Norman, Jeya Prakash Ganesan, Dana B. Sulas-Kern, Roger H. French, Kristopher O. Davis, Steven P. Harvey, Fang Li, Nafis Iqbal, GovindaSamy TamizhMani, and Joseph Karas
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Interconnection ,Materials science ,business.industry ,Photovoltaic system ,Optoelectronics ,Degradation (geology) ,Electrical and Electronic Engineering ,Condensed Matter Physics ,business ,Electronic, Optical and Magnetic Materials ,Characterization (materials science) - Published
- 2022
15. Degradation mechanisms and partial shading of glass-backsheet and double-glass photovoltaic modules in three climate zones determined by remote monitoring of time-series current–voltage and power datastreams
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Menghong Wang, Roger H. French, Jiqi Liu, Alan J. Curran, Erdmut Schnabel, Michael Köhl, and Jennifer L. Braid
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Climate zones ,Reliability (semiconductor) ,Series (mathematics) ,Current voltage ,Renewable Energy, Sustainability and the Environment ,Photovoltaic system ,Environmental science ,General Materials Science ,Shading ,Power (physics) ,Degradation (telecommunications) ,Remote sensing - Abstract
Degradation and partial shading impact the long-term reliability and power production of photovoltaic (PV) modules and power plants. Time-series power ( P mp ) and current–voltage (I-V) curve datastreams from PV modules enable a remote diagnostic approach to quantify active degradation mechanisms and identify partial shading. We study three to nine years of these datastreams, including 3.6 million I-V curves and 36 million P mp values, from eight PV modules, four each of double-glass and glass-backsheet module architectures, located in three distinctly different Koppen-Geiger climate zones, to determine the module’s performance loss rates (PLR), identify active degradation mechanisms and power loss modes, along with partial shading by local objects. Considering both module architectures, PLR results indicate that the BSh climate zone is the most aggressive for module degradation, while the Alpine ET zone is the mildest climate. PLR of double-glass modules located in BWh and BSh climate zones are different due to the significantly greater uniform current loss (ΔPIsc) for double-glass modules in BSh, at a 5% significance level. Power loss for four out of five modules located in the BWh and BSh climates are dominated by uniform current degradation. Statistical analysis of multistep I-V curves detects partial shading experienced by three studied modules with details of the shading profile, the shading Poynting vector diagram for the obstacle’s relative position, shading scenarios, and duration. This work demonstrates how remote monitoring and diagnosis of P mp & I-V time-series of modules can provide quantitative operations and maintenance insights into system performance, degradation mechanisms, and shading.
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- 2021
16. Employing Weibull Analysis and Weakest Link Theory to Resolve Crystalline Silicon PV Cell Strength Between Bare Cells and Reduced- and Full-Sized Modules
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Roger H. French, Sameera Nalin Venkat, Jiqi Liu, Nick Bosco, and Martin Springer
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010302 applied physics ,Materials science ,Photovoltaic system ,02 engineering and technology ,Limiting ,Edge (geometry) ,021001 nanoscience & nanotechnology ,Condensed Matter Physics ,01 natural sciences ,Electronic, Optical and Magnetic Materials ,Stress (mechanics) ,0103 physical sciences ,Fracture (geology) ,Crystalline silicon ,Electrical and Electronic Engineering ,Composite material ,0210 nano-technology ,Link (knot theory) ,Weibull distribution - Abstract
Weibull analysis and weakest link theory are employed to resolve the probability of crystalline silicon PV cell fracture when measured as bare cells and when stressed in reduced- and full-sized modules. Experimental results indicated that the characteristic cell strength is reduced by ∼20% once packaged into the laminate of a one-cell module and loaded in four-point flexure (4PF). This experimental observation was shown consistent with a weakest link theory prediction that the strength limiting flaws reside on the surface of the cell's edge. The analysis is ultimately extended to present the equivalent loading of four-cell modules by uniform pressure and 4PF and a uniformly loaded full-sized module and demonstrates that smaller, representative, modules must be loaded to a much higher level than their parent full-sized modules to achieve an equivalent probability for cell fracture.
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- 2021
17. Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures.
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Abdulkerim Gok, David K Ngendahimana, Cara L Fagerholm, Roger H French, Jiayang Sun, and Laura S Bruckman
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Medicine ,Science - Abstract
Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples' responses, the change in haze (%) depended on individual samples' responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R2 values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction.
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- 2017
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18. A cross-sectional study of the temporal evolution of electricity consumption of six commercial buildings.
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Ethan M Pickering, Mohammad A Hossain, Jack P Mousseau, Rachel A Swanson, Roger H French, and Alexis R Abramson
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Medicine ,Science - Abstract
Current approaches to building efficiency diagnoses include conventional energy audit techniques that can be expensive and time consuming. In contrast, virtual energy audits of readily available 15-minute-interval building electricity consumption are being explored to provide quick, inexpensive, and useful insights into building operation characteristics. A cross sectional analysis of six buildings in two different climate zones provides methods for data cleaning, population-based building comparisons, and relationships (correlations) of weather and electricity consumption. Data cleaning methods have been developed to categorize and appropriately filter or correct anomalous data including outliers, missing data, and erroneous values (resulting in < 0.5% anomalies). The utility of a cross-sectional analysis of a sample set of building's electricity consumption is found through comparisons of baseload, daily consumption variance, and energy use intensity. Correlations of weather and electricity consumption 15-minute interval datasets show important relationships for the heating and cooling seasons using computed correlations of a Time-Specific-Averaged-Ordered Variable (exterior temperature) and corresponding averaged variables (electricity consumption)(TSAOV method). The TSAOV method is unique as it introduces time of day as a third variable while also minimizing randomness in both correlated variables through averaging. This study found that many of the pair-wise linear correlation analyses lacked strong relationships, prompting the development of the new TSAOV method to uncover the causal relationship between electricity and weather. We conclude that a combination of varied HVAC system operations, building thermal mass, plug load use, and building set point temperatures are likely responsible for the poor correlations in the prior studies, while the correlation of time-specific-averaged-ordered temperature and corresponding averaged variables method developed herein adequately accounts for these issues and enables discovery of strong linear pair-wise correlation R values. TSAOV correlations lay the foundation for a new approach to building studies, that mitigates plug load interferences and identifies more accurate insights into weather-energy relationship for all building types. Over all six buildings analyzed the TSAOV method reported very significant average correlations per building of 0.94 to 0.82 in magnitude. Our rigorous statistics-based methods applied to 15-minute-interval electricity data further enables virtual energy audits of buildings to quickly and inexpensively inform energy savings measures.
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- 2017
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19. Evaluation of PV Module Packaging Strategies of Monofacial and Bifacial PERC Using Degradation Pathway Network Modeling
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Sameera Nalin Venkat, Jiqi Liu, Xuanji Yu, Jakob Wegmueller, Kunal Rath, Xinjun Li, Jean-Nicolas Jaubert, Jennifer L. Braid, Roger H. French, and Laura S. Bruckman
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- 2022
20. Overall Performance Losses and Activated Mechanisms in Double Glass and Glass-backsheet Photovoltaic Modules with Monofacial and Bifacial PERC Cells, under Accelerated Exposures
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Jiqi Liu, Sameera Nalin Venkat, Jennifer L. Braid, Xuanji Yu, Brenton Brownell, Xinjun Li, Jean-Nicolas Jaubert, Kaushik Roy Choudhury, Laura S. Bruckman, and Roger H. French
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- 2022
21. Spatiotemporal Modeling of Real World Backsheets Field Survey Data: Hierarchical (Multilevel) Generalized Additive Models
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Raymond J. Wieser, Zelin Zack Li, Stephanie L. Moffitt, Ruben Zabalza, Evan Boucher, Silvana Ayala, Matthew Brown, Xiaohong Gu, Liang Ji, Colleen O'Brien, Adam W. Hauser, Greg S. O'Brien, Xuanji Yu, Roger H. French, Micheal D. Kempe, Jared Tracy, Kausik R. Choudhury, William J. Gambogi, Laura S. Bruckman, and Kenneth P. Boyce
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- 2022
22. Measurement of crack length in width tapered beam experiments
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Liang Ji, Laura S. Bruckman, Yu Wang, Kai-Tak Wan, Joshua Morse, Scott Julien, Kenneth P. Boyce, Gregory S. O'brien, Xiaohong Gu, Michael D. Kempe, Sophie Napoli, Roger H. French, Joshua Eafanti, Adam W. Hauser, and Andrew Fairbrother
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Materials science ,Cantilevered beam ,Fracture mechanics ,030206 dentistry ,02 engineering and technology ,Surfaces and Interfaces ,General Chemistry ,Adhesion ,021001 nanoscience & nanotechnology ,Surfaces, Coatings and Films ,03 medical and health sciences ,0302 clinical medicine ,Fracture toughness ,Mechanics of Materials ,Materials Chemistry ,Tapered beam ,Composite material ,0210 nano-technology - Abstract
The width tapered beam method for measuring fracture toughness has been contemplated for use in measuring the adhesion of photovoltaic materials, is promoted by it being a viable method for use dir...
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- 2020
23. Analytic $I_{\text{sc}}$–$V_{\text{oc}}$ Method and Power Loss Modes From Outdoor Time-Series $I$–$V$ Curves
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Kristopher O. Davis, Eric Schneller, Roger H. French, Jiqi Liu, Jennifer L. Braid, Menghong Wang, and Tyler J. Burleyson
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Physics ,Climate zones ,Power loss ,010504 meteorology & atmospheric sciences ,Series (mathematics) ,Dominant power ,02 engineering and technology ,021001 nanoscience & nanotechnology ,Condensed Matter Physics ,01 natural sciences ,Mismatch loss ,Electronic, Optical and Magnetic Materials ,Combinatorics ,Open source ,Electrical and Electronic Engineering ,0210 nano-technology ,0105 earth and related environmental sciences - Abstract
Utilizing large-scale outdoor $I$ – $V$ data streams, the authors demonstrated a feasible, repeatable method to construct outdoor time-series $I_{\text{sc}}$ – $V_{\text{oc}}$ curves. Resulting Analytic $I_{\text{sc}}$ – $V_{\text{oc}}$ curves are validated with laboratory $Suns$ - $V_{\text{oc}}$ measurements. Comparing $I_{\text{sc}}$ – $V_{\text{oc}}$ with real $I$ – $V$ curves, power loss modes including uniform current loss, recombination loss, series resistance loss, and current mismatch loss can be quantified in units of watts or percent nominal power, making them directly comparable. Above power loss modes are calculated for long-term $I$ – $V$ data from fielded modules in different climate zones, and dominant power loss modes were determined from the resulting time series. This method has been published as an open source package in R programming language.
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- 2020
24. Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images
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Ahmad Maroof Karimi, Justin S. Fada, Roger H. French, Mehmet Koyutürk, Benjamin G. Pierce, Jennifer L. Braid, and Nicholas A. Parrilla
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010302 applied physics ,Maximum power principle ,Equivalent series resistance ,Power station ,Computer science ,business.industry ,Busbar ,010401 analytical chemistry ,Pattern recognition ,Condensed Matter Physics ,01 natural sciences ,Convolutional neural network ,0104 chemical sciences ,Electronic, Optical and Magnetic Materials ,Power (physics) ,Statistical classification ,0103 physical sciences ,Performance prediction ,Artificial intelligence ,Electrical and Electronic Engineering ,business - Abstract
Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current–voltage ( I–V ) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85 $^\circ$ C/85% RH) and thermal cycling (TC) (IEC 61215). In total, 195 pairs of EL images and I–V characteristics were analyzed, yielding 11 700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted- $R^2$ of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted- $R^2$ of up to 0.73.
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- 2020
25. Impact of environmental variables on the degradation of photovoltaic components and perspectives for the reliability assessment methodology
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Greg O'Brien, Sophie Napoli, Scott Julien, Kenneth P. Boyce, Yadong Lyu, Xiaohong Gu, Jae Hyun Kim, Roger H. French, Mengyan Gong, Michael D. Kempe, Yu Wang, Adam W. Hauser, Andrew Fairbrother, Liang Ji, Kai-Tak Wan, and Laura S. Bruckman
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Renewable Energy, Sustainability and the Environment ,020209 energy ,Photovoltaic system ,02 engineering and technology ,021001 nanoscience & nanotechnology ,Arid ,Reliability engineering ,Cracking ,Qualification testing ,Principal component analysis ,Service life ,0202 electrical engineering, electronic engineering, information engineering ,Environmental science ,Degradation (geology) ,General Materials Science ,0210 nano-technology ,Reliability (statistics) - Abstract
Backsheet cracking has been a major issue observed in the field; however, standardized qualification tests, such as IEC61215, are inadequate to reliably identify such failures of PV modules due to the lack of the critical weathering factors applied sequentially or in combination, such as those found in the service environments. To address this problem, we investigated the effects of various environmental variables on the degradation and failure behaviors of the polyamide-based backsheet in PV modules retrieved from five different locations, encompassing a variety of climates, including humid subtropical, hot-summer Mediterranean, tropical savanna climate and hot arid. The correlations between the degradation indicators and the weathering variables were further demonstrated by principle components analysis (PCA). We found strong relationships between: carbonyl formation and reflected solar radiation; hydroxyl formation and module temperature; yellowness and NO2 concentration, while no simple correlation could be found between a specific weathering factor and cracking. By introducing additional stress factors to the aged polyamide-based backsheet films with the novel “fragmentation test”, we successfully reproduced the field cracking behaviour. This study has demonstrated that different degradation modes of PV components respond differently to the environmental stresses encountered in service. Thereby, any accelerated laboratory test based on a single set condition or lacking key environmental variables would be inadequate to assess the long-term performance of PV modules and components. A new reliability-based methodology is proposed to quantitatively link laboratory testing with field results for the service life prediction of PV materials.
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- 2020
26. Data analytics applied to the electricity consumption of office buildings to reveal building operational characteristics
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Arash Khalilnejad, Roger H. French, Alexis R. Abramson, Ethan Pickering, Mohammad A. Hossain, and Rojiar Haddadian
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Consumption (economics) ,Computer science ,business.industry ,020209 energy ,0211 other engineering and technologies ,Building energy ,02 engineering and technology ,Building and Construction ,Interval (mathematics) ,Industrial engineering ,021105 building & construction ,0202 electrical engineering, electronic engineering, information engineering ,Data analysis ,Statistical analysis ,Electricity ,business ,Efficient energy use - Abstract
Rigorous statistical analysis of whole building, 15-minute interval, time series electricity data enables remote insights into buildings’ operational characteristics. We developed select building m...
- Published
- 2020
27. Learnings from developing an applied data science curricula for undergraduate and graduate students
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Roger H. French and Laura S. Bruckman
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Materials science ,Group method of data handling ,Mechanical Engineering ,02 engineering and technology ,Minor (academic) ,010402 general chemistry ,021001 nanoscience & nanotechnology ,Condensed Matter Physics ,01 natural sciences ,Data science ,0104 chemical sciences ,Graduate students ,Mechanics of Materials ,ComputingMilieux_COMPUTERSANDEDUCATION ,Data analysis ,Openness to experience ,Domain knowledge ,General Materials Science ,0210 nano-technology ,Curriculum ,Coding (social sciences) - Abstract
Data science has advanced significantly in recent years and allows scientists to harness large-scale data analysis techniques using open source coding frameworks. Data science is a tool that should be taught to science and engineering students in addition to their chosen domain knowledge. An applied data science minor allows students to understand data and data handling as well as statistics and model development. This move will improve reproducibility and openness of research as well as allow for greater interdisciplinarity and more analyses focusing on critical scientific challenges.
- Published
- 2020
28. In‐situ observation of AlN formation from Ni‐Al solution using an electromagnetic levitation technique
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Andrew J. Loach, Sonoko Hamaya, Justin S. Fada, Masayoshi Adachi, Roger H. French, Yuji Yamagata, Hiroyuki Fukuyama, Jennifer Carter, and Laura G. Wilson
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010302 applied physics ,In situ ,Materials science ,0103 physical sciences ,Materials Chemistry ,Ceramics and Composites ,02 engineering and technology ,Composite material ,021001 nanoscience & nanotechnology ,0210 nano-technology ,01 natural sciences ,Magnetic levitation - Published
- 2020
29. Temperature dependence of crystal growth behavior of AlN on Ni–Al using electromagnetic levitation and computer vision technique
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Masayoshi Adachi, Sonoko Hamaya, Daisuke Morikawa, Benjamin G. Pierce, Ahmad M. Karimi, Yuji Yamagata, Kenji Tsuda, Roger H. French, and Hiroyuki Fukuyama
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Mechanics of Materials ,Mechanical Engineering ,General Materials Science ,Condensed Matter Physics - Published
- 2023
30. Microinverter Thermal Performance in the Real-World: Measurements and Modeling.
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Mohammad Akram Hossain, Yifan Xu, Timothy J Peshek, Liang Ji, Alexis R Abramson, and Roger H French
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Medicine ,Science - Abstract
Real-world performance, durability and reliability of microinverters are critical concerns for microinverter-equipped photovoltaic systems. We conducted a data-driven study of the thermal performance of 24 new microinverters (Enphase M215) connected to 8 different brands of PV modules on dual-axis trackers at the Solar Durability and Lifetime Extension (SDLE) SunFarm at Case Western Reserve University, based on minute by minute power and thermal data from the microinverters and PV modules along with insolation and environmental data from July through October 2013. The analysis shows the strengths of the associations of microinverter temperature with ambient temperature, PV module temperature, irradiance and AC power of the PV systems. The importance of the covariates are rank ordered. A multiple regression model was developed and tested based on stable solar noon-time data, which gives both an overall function that predicts the temperature of microinverters under typical local conditions, and coefficients adjustments reecting refined prediction of the microinverter temperature connected to the 8 brands of PV modules in the study. The model allows for prediction of internal temperature for the Enphase M215 given similar climatic condition and can be expanded to predict microinverter temperature in fixed-rack and roof-top PV systems. This study is foundational in that similar models built on later stage data in the life of a device could reveal potential influencing factors in performance degradation.
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- 2015
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31. Degradation of Monofacial Double Glass and Glass Backsheet Photovoltaic Modules with Multiple Packaging Combinations
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Ben Yu, Jean-Nicolas Jaubert, Roger H. French, Laura S. Bruckman, Jennifer L. Braid, Jiqi Liu, Brent Brownell, Sameera Nalin Venkat, and Xinjun Li
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chemistry.chemical_compound ,Power loss ,Interconnection ,Materials science ,chemistry ,Equivalent series resistance ,Transmission loss ,Photovoltaic system ,Degradation (geology) ,Composite material ,Elastomer ,Polyolefin - Abstract
The long-term reliability of photovoltaic (PV) modules is essential to decrease the levelized cost of electricity and is dependent on module packaging choices. In this paper, we study the degradation of double glass (DG) and glass-backsheet (GB) PV modules with ethylene-vinyl acetate (EVA) and polyolefin elastomer (POE) encapsulants using multicrystalline PERC cells under accelerated exposures including modified damp heat (mDH) and mDH with full-spectrum light (FSL). The results showed that the modules with opaque rear encapsulant have greater power loss on average than those with UV-cutoff rear encapsulant for each module type. The dominant degradation mechanism was series resistance (R s ) increase indicating interconnect corrosion for most module types. In addition to the increased R s , GB modules with UV-cutoff rear encapsulant experienced power loss by transmission loss, and the POE_GB type under mDH+FSL also had more cell shunting. For modules with opaque rear encapsulant, the POE_DG type under mDH+FSL had power loss dominated by transmission loss.
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- 2021
32. Spatio-Temporal Modeling of Field Surveyed Backsheet Degradation
- Author
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Kenneth P. Boyce, Xiaohong Gu, Greg O'Brien, Colleen O'Brien, Raymond J. Wieser, Matthew Brown, Liang Ji, Stephanie L. Moffitt, Adam W. Hauser, Roger H. French, Kausik R. Choudhury, Evan Boucher, William J. Gambogi, Laura S. Bruckman, Ruben Zabalza, Jared Tracy, Silvana Ayala, Kunal Rath, and Micheal D. Kempe
- Subjects
Scale (ratio) ,Photovoltaic system ,Elevation ,Environmental science ,Magnitude (mathematics) ,Satellite ,Spatial dependence ,Field (geography) ,Data modeling ,Remote sensing - Abstract
Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Lab based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Field surveyed data is critical to assess the performance of component lifetime. Using a documented field survey protocol, 13 field surveys where conducted. Each measurement is encoded with it’s spatial location in respect to the other modules. By combining field survey data on degradation predictors with real time satellite weather data, data-driven predictive models of backsheet degradation were trained. LOESS models were constructed to investigate the spatial dependence of measurements. It was found that micro-climatic effects like tree-lines, ground surface changes, and elevation changes effected the magnitude and variance of the measurements. A GAM model was created to predict the value of degradation based on measured predictors. The model includes variables on the climate of the system and the location of each measurement in the PV mounting structure. The model performed well with an adj.R2 of 0.95 for yellowness index prediction. The model was cross-validated using k-folds.
- Published
- 2021
33. Degradation of PERC and Al-BSF Cells with UV Cutoff and White Variations of EVA and POE Encapsulant
- Author
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Bryan D. Huey, Thomas Moran, Roger H. French, Jean-Nicolas Jaubert, Jennifer L. Braid, Laura S. Bruckman, Dylan J. Colvin, Ben Yu, Kris Davis, Brent Brownell, Nafis Iqbal, and Alan J. Curran
- Subjects
Materials science ,Degradation (geology) ,sense organs ,Damp heat ,Composite material - Abstract
To assess the reliability of PERC cells compared to Al-BSF in a commercial setting minimodules with cell and encapsulant combinations are compared in accelerated exposure. In both modified damp heat and modified damp heat with full spectrum light exposures, white EVA samples showed a higher susceptibility for metallization corrosion degradation than all other encapsulants. Al-BSF cells in particular showed higher power loss than PERC cells with white EVA. It was observed that the degree of degradation had a strong significance on the manufacturer of the white EVA encapsulant. In both exposures the encapsulant was a much stronger predictor of degradation than cell type. For modules with the same encapsulant, PERC cells showed the higher performance or were comparable to Al-BSF cells for all but one case.
- Published
- 2021
34. Degradation Pathway Modeling of PV Minimodule Variants with Different Packaging Materials Under Indoor Accelerated Exposures
- Author
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Roger H. French, Laura S. Bruckman, Jakob Wegmueller, Jean-Nicolas Jaubert, Brian Gould, Jiqi Liu, Sameera Nalin Venkat, Xinjun Li, Ben Yu, and Jennifer L. Braid
- Subjects
chemistry.chemical_compound ,Materials science ,chemistry ,Photovoltaic system ,Degradation (geology) ,Ethylene-vinyl acetate ,Relative humidity ,Composite material ,Elastomer ,Degradation pathway ,Corrosion ,Polyolefin - Abstract
Network structural equation modeling has been used for degradation modeling of glass/backsheet (GB) and double glass (DG) PERC PV minimodules, made by CSI and CWRU. The encapsulants used were ethylene vinyl acetate (EVA) and polyolefin elastomer (POE). The exposures included modified damp heat (80°C and 85% relative humidity), with and without full spectrum light. Each exposure cycle consists of 2520 hours, 5 steps of 504 hours each. The data from I-V and Suns-V oc was used in the analysis. We observe that most DG minimodules exhibit stability in power with exposure time and GB minimodules by CWRU showed a power loss of 5-6% on average due to corrosion.
- Published
- 2021
35. Representative Modules for Accelerated Thermal Cycling and Static Load Testing
- Author
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Nick Bosco, Roger H. French, Martin Springer, Timothy J. Silverman, Jiqi Liu, and Sameera Nalin Venkat
- Subjects
Load testing ,Materials science ,Structural mechanics ,Soldering ,Fracture (geology) ,Temperature cycling ,Crystalline silicon ,Composite material ,computer.software_genre ,computer ,Finite element method ,Weibull distribution - Abstract
In this work, we explore the influence of module size on the rate of interconnect solder bond thermomechanical fatigue (TMF) damage and the probability of cell fracture. For the solder bond TMF damage evaluation, structural mechanics models of crystalline silicon PV models are created to solve with the Finite Element Method. For the probability of cell fracture evaluation, Weibull analysis and weakest link theory are employed to resolve the probability of crystalline silicon PV cell fracture when measured as bare cells and when stressed in reduced- and full-sized modules. Results conclusively demonstrate that the rate of solder bond TMF damage is independent of module size, interconnect location across the cell and cell location across the module and that smaller, representative, modules must be loaded to a much higher level than their parent full-sized modules to achieve an equivalent driving force for cell fracture.
- Published
- 2021
36. Toward Findable, Accessible, Interoperable and Reusable (FAIR) Photovoltaic System Time Series Data
- Author
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Yinghui Wu, Roger H. French, Jennifer L. Braid, Arafath Nihar, Mehmet Koyutürk, Laura S. Bruckman, Alan J. Curran, and Ahmad Maroof Karimi
- Subjects
Metadata ,Data model (ArcGIS) ,Database ,Computer science ,Scalability ,Interoperability ,Maintainability ,ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS ,Information repository ,Machine-readable data ,computer.software_genre ,computer ,Reusability - Abstract
We present the application of FAIR principles to photovoltaic time series data to increase their reusability within the photovoltaic research community. The main requirements for a "FAIRified" dataset is to have a clearly defined data format, and to make accessible all metadata for this dataset to humans and machines. To achieve FAIRification, we implement a data model that separates the photovoltaic data and its metadata. The metadata and their descriptions are registered on a data repository in a human and machine readable format, using JSON-LD. Also, secure APIs are developed to access photovoltaic data. This approach has long term scalability and maintainability.
- Published
- 2021
37. Impact of surface passivation on UV stability of bifacial mc-Si PERC solar modules
- Author
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Jean-Nicolas Jaubert, Bryan D. Huey, Ben X. J. Yu, Nafis Iqbal, Huade Wu, Guangchun Zhang, Jian Wu, Jennifer L. Braid, Fangdan Jiang, Laura S. Bruckman, Dylan J. Colvin, Roger H. French, Kristopher O. Davis, Thomas Moran, and Alan J. Curran
- Subjects
Thermal oxidation ,Materials science ,Silicon ,Passivation ,business.industry ,chemistry.chemical_element ,law.invention ,chemistry.chemical_compound ,chemistry ,Silicon nitride ,Plasma-enhanced chemical vapor deposition ,law ,Solar cell ,Optoelectronics ,Quantum efficiency ,business ,Sheet resistance - Abstract
Many research show that UV degrades the solar cell and module power. In this work, we study the UV stability of mc-Si bifacial PERC solar modules with different industrial silicon nitride (SiNx) passivation. We find that with exposure to UV at 60 °C, both VOC and JSC decrease then stabilize. The quantum efficiency analysis shows decreased response in both short (blue loss) and long (base collection loss) wavelength range. It infers that deteriorations were not only at passivation layers, but also in the silicon bulk, caused by UV-induced degradation and possible LeTID. No significant difference in degradation has been found between front and rear passivation, or between different PECVD, respectively. While thermal oxidation process employed in mass production effectively improved UV resistance.
- Published
- 2021
38. Properties of PV Cell Fractures and Effects on Performance of Al-BSF and PERC Modules
- Author
-
Roger H. French, Carolina M. Whitaker, Jennifer L. Braid, and Benjamin G. Pierce
- Subjects
Electrical isolation ,Power loss ,Cracking ,Materials science ,mental disorders ,Photovoltaic system ,Composite material ,Lead (electronics) - Abstract
Cell cracking in PV modules can lead to a variety of changes in module operation, with vastly different performance degradation based on the type and severity of the cracks. In this work, we demonstrate automated measurement of cell crack properties from electroluminescence images, and correlate these properties with current-voltage curve features on 35 four-cell Al-BSF and PERC mini-modules showing a range of crack types and severity. Power loss in PERC modules was associated with more total crack length, resulting in electrical isolation of cell areas and mild shunting and recombination. Many of the Al-BSF modules suffered catastrophic power loss due to crack-related shunts. Mild power loss in Al-BSF modules was not as strongly correlated with total crack length; instead crack angles and branching were better indicators of module performance for this cell type.
- Published
- 2021
39. Automated Pipeline for Photovoltaic Module Electroluminescence Image Processing and Degradation Feature Classification
- Author
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Roger H. French, Ahmad Maroof Karimi, Mohammad A. Hossain, Justin S. Fada, Shuying Yang, Timothy J. Peshek, and Jennifer L. Braid
- Subjects
010302 applied physics ,Computer science ,business.industry ,020209 energy ,Image processing ,Pattern recognition ,02 engineering and technology ,Condensed Matter Physics ,01 natural sciences ,Thresholding ,Convolutional neural network ,Cross-validation ,Electronic, Optical and Magnetic Materials ,Random forest ,Support vector machine ,Test set ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,Feature (machine learning) ,Artificial intelligence ,Electrical and Electronic Engineering ,business - Abstract
An automated data analysis pipeline is developed to preprocess electroluminescence (EL) module images, and parse the images into individual cells to be used as an input for machine learning algorithms. The dataset used in the study includes EL images of three 60 cell modules from each of five commercial brands at six steps of damp heat exposure, from 500 to 3000 h. Preprocessing of the original raw EL images includes lens distortion correction, filtering, thresholding, convex hull, regression fitting, and perspective transformation to produce planar indexed module and single cell images. Parsing of PV cells from each of the preprocessed 90 EL module images gives us 5400 cell images, which are function of module brand and damp heat exposure step. From the dataset, two unique degradation categories (“cracked” and “corroded”) were observed, while cells that did not degrade were classified as “good.” For supervised machine learning modeling, cell images were sorted into these three classes yielding 3550 images. A training and testing framework with 80:20 sampling ratio was generated using stratified sampling. Three machine learning algorithms (support vector machine, Random Forest, and convolutional neural network) were trained and tuned independently on the training set and then given the test set to predict the scores for each of the three models. Five-fold cross validation was done on training set to tune hyper-parameters of the models. Model prediction scores showed that convolutional neural network outperforms support vector machine and Random Forest for supervised PV cell classification.
- Published
- 2019
40. Data-Driven $I$–$V$ Feature Extraction for Photovoltaic Modules
- Author
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Roger H. French, Erdmut Schnabel, Xuan Ma, Jenny Brynjarsdottir, Michael Köhl, Wei-Heng Huang, and Jennifer L. Braid
- Subjects
Equivalent series resistance ,Maximum power principle ,Transcendental equation ,020209 energy ,Feature extraction ,Photovoltaic system ,02 engineering and technology ,021001 nanoscience & nanotechnology ,Condensed Matter Physics ,Topology ,Electronic, Optical and Magnetic Materials ,0202 electrical engineering, electronic engineering, information engineering ,Point (geometry) ,Electrical and Electronic Engineering ,0210 nano-technology ,Scaling ,Voltage ,Mathematics - Abstract
In research on photovoltaic (PV) device degradation, current–voltage ( $I$ – $V$ ) datasets carry a large amount of information in addition to the maximum power point. Performance parameters such as short-circuit current, open-circuit voltage, shunt resistance, series resistance, and fill factor are essential for diagnosing the performance and degradation of solar cells and modules. To enable the scaling of $I$ – $V$ studies to millions of $I$ – $V$ curves, we have developed a data-driven method to extract $I$ – $V$ curve parameters and distributed this method as an open-source package in R. In contrast with the traditional practice of fitting the diode equation to $I$ – $V$ curves individually, which requires solving a transcendental equation, this data-driven method can be applied to large volumes of $I$ – $V$ data in a short time. Our data-driven feature extraction technique is tested on $I$ – $V$ curves generated with the single-diode model and applied to $I$ – $V$ curves with different data point densities collected from three different sources. This method has a high repeatability for extracting $I$ – $V$ features, without requiring knowledge of the device or expected parameters to be input by the researcher. We also demonstrate how this method can be applied to large datasets and accommodates nonstandard $I$ – $V$ curves including those showing artifacts of connection problems or shading where bypass diode activation produces multiple “steps.” These features together make the data-driven $I$ – $V$ feature extraction method ideal for evaluating time-series $I$ – $V$ data and analyzing power degradation mechanisms in PV modules through cross comparisons of the extracted parameters.
- Published
- 2019
41. Generalized Spatio-Temporal Model of Backsheet Degradation From Field Surveys of Photovoltaic Modules
- Author
-
Yu Wang, Scott Julien, Kenneth P. Boyce, Xiaohong Gu, Kai-Tak Wan, Roger H. French, Lucas S. Fridman, Adam W. Hauser, Sophie Napoli, Alan J. Curran, Liang Ji, Nicholas R. Wheeler, Andrew Fairbrother, Laura S. Bruckman, Gregory S. O'brien, Wei-Heng Huang, and Michael D. Kempe
- Subjects
020209 energy ,Photovoltaic system ,Irradiance ,02 engineering and technology ,021001 nanoscience & nanotechnology ,Condensed Matter Physics ,Electronic, Optical and Magnetic Materials ,Rack ,0202 electrical engineering, electronic engineering, information engineering ,Environmental science ,Degradation (geology) ,Electrical and Electronic Engineering ,0210 nano-technology ,Pv power ,Remote sensing - Abstract
Photovoltaic (PV) module backsheets degrade at different rates because of the specific polymeric materials and the local exposure environments of the installations. Studies of real-world backsheet degradation provide valuable information to understand backsheet degradation and failure. Field surveys of PV module backsheets were conducted on 1310 modules in four commercial PV power plant sites with different exposure times. The backsheet's local exposure environment is determined by its location along the rack length and depth, the modules’ elevation above the ground, and the ground cover albedo. Backsheets that are installed at the ends of the module rack length exhibit larger degradation rates (with yellowness index difference of 1.14 $\pm$ 0.45 and 7.80 $\pm$ 1.3 for backsheets of poly (ethylene terephthalate) and poly (ethylene naphthalate) as the air-side layer, respectively) than backsheets in the center section of the rack. A generalized spatio-temporal model was developed to predict the large-scale backsheet degradation of different backsheet polymers across time/age and location in the PV power plant rack. The model utilizes a cubic-spline relationship between backsheet degradation and rack length, and a quadratic relationship between backsheet discoloration and rack depth. This generalized spatio-temporal model predicts the outdoor backsheet degradation with an adjusted- $R^2$ range between 0.31 to 0.89. The similarity between this model and the spatial variation of the rear-side irradiance, indicates that the irradiance plays a significant role in outdoor backsheet degradation. The generalized spatio-temporal model can be used to evaluate large scale PV backsheet performance, as well as a guidance for PV site designers and operations and maintenance crews.
- Published
- 2019
42. Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities
- Author
-
Ross Brindle, Bryce Meredig, Roger H. French, Elizabeth C. Dickey, Krishna Rajan, Laura M. Bartolo, Changwon Suh, Sarah Lichtner, Charlie Spahr, Eileen De Guire, Adama Tandia, Edgar Lara-Curzio, Susan B. Sinnott, Mark Mecklenborg, Jeffrey M. Rickman, Emmanuel Maillet, Richard Weber, Ulrich Fotheringham, Logan Ward, Ram Devanathan, Justin Fessler, Martin P. Harmer, and John C. Mauro
- Subjects
Engineering ,Glass-ceramic ,business.industry ,Engineering physics ,law.invention ,Science research ,law ,Informatics ,visual_art ,Materials Chemistry ,Ceramics and Composites ,visual_art.visual_art_medium ,Ceramic ,business - Published
- 2019
43. Mapping Multivariate Influence of Alloying Elements on Creep Behavior for Design of New Martensitic Steels
- Author
-
J.A. Hawk, Roger H. French, Vyacheslav Romanov, Amit K. Verma, Jennifer Carter, and Laura S. Bruckman
- Subjects
010302 applied physics ,Multivariate statistics ,Materials science ,Structural material ,Metallurgy ,Alloy ,0211 other engineering and technologies ,Metals and Alloys ,02 engineering and technology ,engineering.material ,Condensed Matter Physics ,Microstructure ,01 natural sciences ,Lasso regression ,Creep ,Mechanics of Materials ,Martensite ,0103 physical sciences ,Linear regression ,engineering ,021102 mining & metallurgy - Abstract
Heritage data for the class of 9 to 12 wt pct Cr steels are studied using data science to quantify the statistically significant relationships among multiple processing/microstructure and performance variables. The effort is undertaken to find new martensitic steels for creep life of $$10^5{{\text { hours}}}$$ or greater at 650 °C and 100 MPa using machine learning. Linear regression and lasso regression were utilized to identify alloying elements that contribute towards better creep strength. Visualization techniques such as t-distributed stochastic neighbor embedding and pair-wire element specific comparisons were utilized to explore information gaps that exist within the data and are in conflict with existing domain knowledge. Combining all results suggest that the next alloy design to be explored should be 9 wt pct Cr with high W (2 to 3 wt pct) and high Co (2 to 3 wt pct) for creep life of $$10^5\,\,{\text { hours}}$$ or greater at 650 °C, 100 MPa.
- Published
- 2019
44. Materials data analytics for 9% Cr family steel
- Author
-
Jeffrey A. Hawk, Vyacheslav Romanov, Amit K. Verma, Narayanan Krishnamurthy, Jennifer Carter, Roger H. French, and Laura S. Bruckman
- Subjects
Computer science ,Alloy ,Data analysis ,engineering ,Data mining ,engineering.material ,computer.software_genre ,Cluster analysis ,computer ,Analysis ,Computer Science Applications ,Information Systems - Published
- 2019
45. Designing New Materials for Photovoltaics: Opportunities for Lowering Cost and Increasing Performance through Advanced Material Innovations
- Author
-
Tadanori Tanahashi, Gernot Oreski, K.A. Berger, Joshua S. Stein, Jan Vedde, Roger H. French, Samuli Ranta, Gabriele C. Eder, Laura S. Bruckman, and Karl-Anders Weiss
- Subjects
Materials science ,Photovoltaics ,business.industry ,New materials ,business ,Manufacturing engineering - Published
- 2021
46. Geoinformatics
- Author
-
Jeffrey M. Yarus, Jordan M. Yarus, and Roger H. French
- Published
- 2021
47. Needs, Challenges and Approaches for new Service Life Estimation Models for PV Modules – Results from IEA PVPS Task 13 Subtask 1.4
- Author
-
Karl-Anders Weiß, Ismail Kaaya, Laura Bruckman, Roger H. French, Gernot Oreski, and Tadanori Tanahashi
- Published
- 2021
48. Direct nanoscale mapping of open circuit voltages at local back surface fields for PERC solar cells
- Author
-
Menghong Wang, Bryan D. Huey, Roger H. French, Jianfang Dai, Eric Schneller, Alexandra Longacre, Alan J. Curran, Kristopher O. Davis, Michael Martin, Laura S. Bruckman, Jean-Nicolas Jaubert, Jennifer L. Braid, Oleg Kolosov, and Thomas Moran
- Subjects
Materials science ,business.industry ,Open-circuit voltage ,020502 materials ,Mechanical Engineering ,Photovoltaic system ,02 engineering and technology ,Conductive atomic force microscopy ,law.invention ,Monocrystalline silicon ,0205 materials engineering ,Mechanics of Materials ,law ,Photovoltaics ,Solar cell ,Optoelectronics ,General Materials Science ,business ,Common emitter ,Voltage - Abstract
The open circuit voltage (VOC) is a critical and common indicator of solar cell performance as well as degradation, for panel down to lab-scale photovoltaics. Detecting VOC at the nanoscale is much more challenging, however, due to experimental limitations on spatial resolution, voltage resolution, and/or measurement times. Accordingly, an approach based on Conductive Atomic Force Microscopy is implemented to directly detect the local VOC, notably for monocrystalline Passivated Emitter Rear Contact (PERC) cells which are the most common industrial-scale solar panel technology in production worldwide. This is demonstrated with cross-sectioned monocrystalline PERC cells around the entire circumference of a poly-aluminum-silicide via through the rear emitter. The VOC maps reveal a local back surface field extending ~ 2 μm into the underlying p-type Si absorber due to Al in-diffusion as designed. Such high spatial resolution methods for photovoltaic performance mapping are especially promising for directly visualizing the effects of processing parameters, as well as identifying signatures of degradation for silicon and other solar cell technologies.
- Published
- 2020
49. PV Cell Cracks and Impacts on Electrical Performance
- Author
-
Ahmad Maroof Karimi, Jennifer L. Braid, Benjamin G. Pierce, Roger H. French, and Carolina M. Whitaker
- Subjects
Electrical isolation ,Cracking ,business.industry ,mental disorders ,Fracture (geology) ,Electrical performance ,Statistical analysis ,Structural engineering ,Tracing ,Electroluminescence ,business ,Image properties - Abstract
Cell cracking in PV modules can lead to a variety of changes in the modules operation, with vastly different performance degradation based on the type and severity of crack. In this work, we correlate cell crack metrics in images with current-voltage (I-V) curve features on a sample set of 38 four-cell Al-BSF and PERC mini-modules showing a range of fracture and electrical properties. Impacts of cracking on electrical performance demonstrated in this work include cell shunting and electrical isolation of cell regions, shown with electroluminescence (EL) images and I-V tracing. Cracks and other EL image properties are quantified with algorithmic computer vision techniques, and correlated with I-V properties at the cell and module levels.
- Published
- 2020
50. Mechanistic Insights to Degradation of PERC Minimodules with Differentiated Packaging Materials & Module Architectures
- Author
-
Jianfang Dai, Jennifer Carter, Laura S. Bruckman, Roger H. French, Sameera Nalin Venkat, Jennifer L. Braid, Jiqi Liu, Jean-Nicolas Jaubert, and Nick Bosco
- Subjects
Materials science ,Silicon ,chemistry.chemical_element ,Ethylene-vinyl acetate ,02 engineering and technology ,Electroluminescence ,010402 general chemistry ,021001 nanoscience & nanotechnology ,Elastomer ,01 natural sciences ,0104 chemical sciences ,Polyolefin ,chemistry.chemical_compound ,chemistry ,Degradation (geology) ,Composite material ,0210 nano-technology - Abstract
In this paper, we study the degradation behavior of glass-backsheet (GB) and double glass (DG) multicrystalline silicon monofacial PERC minimodules, fabricated using polyolefin elastomer (POE) and ethylene vinyl acetate (EVA) encapsulants, undergoing exposures of modified damp-heat (80°C / 85% RH), with and without full spectrum light. The completed exposure time, at this time, is 1512 hours with each step of 504 hours. Stepwise measurements are conducted throughout exposures including current-voltage (I-V) curves, $Suns-V_{oc}$ , electroluminescence (EL) images, and four-point proof loading with EL for tracking changes in mechanical properties in encapsulants by cell fracture probability. The results show that GB minimodules of both encapsulants, compared to DG minimodules, experience more changes in features of I-V and $Suns-V_{oc}$ measurements. There is no development of PV cell cracking during proof loading as seen from EL images and the load versus load line displacement plots currently.
- Published
- 2020
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