Document Type
Article
Publication Date
6-10-2013
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/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.
Keywords
photovoltaics, statistical analytics, lifetime and degradation science, structural equation modeling
Publication Title
IEEE Access
Volume
1
First Page
384
Last Page
403
Funder
Underwriter Laboratories
Rights
© 2013 IEEE. This article was published under the IEEE Open Access Publishing Agreement (OAPA). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. IEEE permits non-commercial text and data mining of articles published with the Open Access Publishing Agreement (OAPA).
Recommended Citation
L. S. Bruckman et al., "Statistical and Domain Analytics Applied to PV Module Lifetime and Degradation Science," in IEEE Access, vol. 1, pp. 384-403, 2013, © 2013 IEEE doi: 10.1109/ACCESS.2013.2267611.