实际暴露模块与背板材料加速暴露之间背板退化的相互关系(会议报告)

L. Bruckman, R. French, Yu Wang, M. Kempe, A. A. Lefebvre, X. Gu, Liang Ji, K. Wan, C. Flueckiger
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引用次数: 2

摘要

板材是光伏组件的关键聚合物组分,了解其降解是预测光伏组件寿命的必要条件。我们正在开发一个背板预测测试和一个基于分析技术和数据流的实时数据的模型,这些数据适用于室外和室内光伏模块背板研究,并辅以气象数据、气候和品牌/模型以及其他可获取的信息。预测测试和模型将指定室内和室外暴露和评估数据获取标准、变量选择、时间持续时间和变化,以便能够预测不同气候带的背板性能。该backsheet性能预测基于已定义的backsheet在现场的失效,并通过跟踪backsheet在现场的退化来量化,从而确定退化率。背板寿命性能预测测试和模型将使用压力源/机制/响应框架开发,其中所有数据被分类为压力源、机制和性能(响应)变量,并表示为离散的时间点数据集。我们将开发并验证这些加速室内暴露和评估以及模型并交叉关联室外和加速室内暴露和评估。评估技术包括非破坏性光谱学和显微镜技术以及破坏性技术,并将提供用于预测建模的预定义变量的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross-correlation of backsheet degradation between real-world exposed modules and accelerated exposures of backsheet materials (Conference Presentation)
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.
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