From Event: SPIE Optics + Photonics for Sustainable Energy, 2016
heets are a key polymeric component of a PV module and understanding its degradation is necessary to be able to predict the lifetime of PV modules. We are developing a backsheet predictive tests and a model based on point- in-time data from analytical techniques and datastreams that are applicable to both outdoor and indoor PV module backsheet studies and are supplemented with meteorology data, climatic and brand/model, and other accessible information. The predictive tests and models will specify indoor and outdoor exposure and evaluation data acquisition criteria, variable selection, and temporal duration and variation so as to be able to predict backsheet performance in various climatic zones. This backsheet performance prediction is based on defined backsheet failures in the field, and is quantified by tracking backsheet degradation in the field so as to determine degradation rates. The backsheet lifetime performance predictive tests and models, will be developed using a Stressor / Mechanism / Response framework in which all data are categorized as stressor, mechanism and performance (response) variables and are represented as discrete points-in-time datasets. We will develop and validate these accelerated indoor exposures and evaluations and models and cross-correlate the outdoor and accelerated indoor exposures and evaluations. The evaluation techniques include nondestructive spectroscopy and microscopy techniques and destructive techniques and will provide data in predefined variables, which are used in the predictive modeling.
Laura S. Bruckman, Roger H. French, Yu Wang, Michael D. Kempe, Amy A. Lefebvre, Xiaohong Gu, Liang Ji, Kai-Tak Wan, and Christopher Flueckiger, "Cross-correlation of backsheet degradation between real-world exposed modules and accelerated exposures of backsheet materials
(Conference Presentation)," Proc. SPIE 9938, Reliability of Photovoltaic Cells, Modules, Components, and Systems IX, 993805 (Presented at SPIE Optics + Photonics for Sustainable Energy: August 28, 2016; Published: 2 November 2016); https://doi.org/10.1117/12.2238162.5167077708001.
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