Performance of a photovoltaic (PV) module is related to the micro-environment around the module. The position of photovoltaic modules in an array row have a large effect on the yellowing and gloss of PV module backsheet exposed in Dfa climatic zone (Gaithersburg, MD) with a polyethylene naphthalate (PEN) outer layer. <Stress/ Response< models of yellowing and gloss-losing as function of location parameters of module, including the shed, row, measurement position in a same module and the distance of module location to the row center, are under development. The module installation height had the greatest influence on degradation of PEN PV backsheet in the Dfa climatic zone. The module backsheets at the end of an array have higher degradation rate (edge effect). The edge effect decreases with increasing of module installation heights.
The selection of polymeric materials utilized in photovoltaic (PV) modules has changed relatively little since the inception of the PV industry, with ethylene-vinyl acetate (EVA), polyethylene terephthalate (PET), and fluoropolymer-based laminates being the most widely adopted primary components of the encapsulant and backsheet materials. The backsheet must serve to electrically insulate the solar cells and protect them from the effects of weathering. Due to continued downward pressure on cost, other polymeric materials are being formulated to withstand outdoor exposure for use in backsheets to replace either the PET film, the fluoropoymer film, or both. Because of their relatively recent deployment, less is known about their reliability and if they are durable enough to fulfill the ≥25 year warranties of current PV modules. This work presents a degradation analysis of field-exposed modules with polyamide- and polyester-based backsheets. Modules were exposed for up to five years in different geographic locations: USA (Maryland, Ohio), China, and Italy. Surface and cross-sectional analysis included visual inspection, colorimetry, glossimetry, and Fourier-transform infrared spectroscopy. Each module experienced different types of degradation depending on the exposure site, even for the same material and module brand. For instance, the polyamide-based backsheet experienced hairline cracking and greater yellowing and chemical changes in China (Changsu, humid subtropical climate), while in Italy (Rome, hot-summer Mediterranean climate) it underwent macroscopic cracking and greater losses in gloss. Spectroscopic studies have permitted identification of degradation products and changes in polymer structure over time. Comparisons are made to fielded modules with fluoropolymer-based backsheets, as well as backsheet materials in accelerated laboratory exposures. Implications for qualification testing and service life prediction of the non-fluoropolymer-based backsheets are discussed.
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.