Title :
Statistical and Domain Analytics Applied to PV Module Lifetime and Degradation Science
Author :
Bruckman, L.S. ; Wheeler, N.R. ; Ma, Jiaxin ; Wang, Eddie ; Wang, C.K. ; Chou, I. ; Jiayang Sun ; French, R.H.
Author_Institution :
Dept. of Mater. Sci. & Eng., Case Western Reserve Univ., Cleveland, OH, USA
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/m2 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 ace- ate (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 R2 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 :
modules; polymers; statistical analysis; UV preconditioning exposure; accelerated testing data; applied stresses; component metrics; damp heat exposure; degradation mechanisms; degradation modes; degradation pathway; degradation rates; domain analytics; ethylene vinyl acetate hydrolysis; fluorinated polyester backsheets; linear change point; material science; model coefficients; module performance; pathway diagram; photovoltaic module degradation science; photovoltaic module lifetime; polyester terephthalate; rank ordered lifetime; reproducible open data science; sound statistical analysis; statistical analytics; structural equation modeling technique generalization; system performance metrics; system response; temperature 60 degC; temperature 85 degC; time 1890 h; ultraviolet light; variable transformations; wavelength 280 nm to 400 nm; Degradation; Heating; Mathematical model; Measurement uncertainty; Photovoltaics; Structural engineering; Temperature measurement; Photovoltaics; lifetime and degradation science; statistical analytics; structural equation modeling;
Journal_Title :
Access, IEEE
DOI :
10.1109/ACCESS.2013.2267611