Qualification of polymeric components for use in PV modules

Möller, Kenneth

doi:10.1117/12.893451

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…It is calculated using the diffuse ( T diffuse ) and total transmission ( T total ) in the spectral range from 380 to 780 nm as shown in Eq 2 . Changes in YI and haze (%) are seen as an early indicator of chemical changes in the polymer due to chromophores or crystallites that are formed during weathering degradation, and precursors to embrittlement and mechanical failure [ 47 ]. All samples were evaluated at 168 hrs independent of what exposure cycle the samples were currently in.…”

Section: Experimental and Statistical Methodsmentioning

confidence: 99%

Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures

et al. 2017

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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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Section: Experimental and Statistical Methodsmentioning

confidence: 99%

Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures

et al. 2017

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“…(b): As the exposure time increases, the acetate C=O (1735 cm −1 ) peak decreases continuously, whereas the aldehyde/ketone C=O (1716 cm −1 ) and O-H (near 3400 cm −1 ) peaks increase. This results from decomposition of vinyl acetate in the EVA and the formation of aldehydes, ketones and alcohols [50]. Ester ether C-O-C /overall CH absorbance ratios were calculated to measure the relative acetate content [47].…”

Section: ) Mechanistic Responsesmentioning

confidence: 99%

Statistical and Domain Analytics Applied to PV Module Lifetime and Degradation Science

Bruckman

Wheeler

et al. 2013

IEEE Access

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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/m 2 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 R 2 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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