In this example, full size PV modules were exposed to damp heat
conditions (relative humidity of 85% and a chamber temperature of
85\(^\circ\)C) for 4000 hours and the
degradation in PV power output was measured at specific time intervals,
or time steps.
In addition, some modules were withdrawn from the study at these time
steps, to produce a retained sample library with samples available for
each time step. These retained samples were dismantled destructively in
order to determine the changes occurred in the module materials. In the
resulting netSEM model, time was used as a proxy for damp heat exposure
stress (\(S\)) variable and \(P_{max}\) (maximum power generated by the
modules) was used as the performance level response (\(R\)) variable, several mechanistic
variables (\(M\)) were obtained through
chemical evaluation methods, including FTIR and TGA on EVA encapsulant.
This example also demonstrate how to add latent variables.
## Load the pv module data set
data(PVmodule)
## Run netSEMp1 model
ans1 <- netSEMp1(PVmodule)
#plot network model with latent variable
plot(ans1, style = F, cutoff = c(0.3, 0.6, 0.9),
latent=c('EVA_hyd', 'Hac'))
## Run netSEMp2 model
ans2 <- netSEMp2(PVmodule)
## Plot the network model for principle 2
plot(ans2, style = F, cutoff = c(0.3, 0.6, 0.9),
latent=c('EVA_hyd', 'Hac'))
PVmodule netSEMp1 model
PVmodule netSEMp2 model
“Time” is the endogenous and all other variables are considered as exogenous. Latent variable \(Hac\) is the formation of acetic acid measured through thermogravimetric analysis (TGA), latent variable \(EVA\_hyd\) stands for EVA hydrolysis, which is tracked by infrared signal corresponding to that, noted by \(IREVA\), and \(P_{max}\) is the maximum power generated by the PV modules.
The analysis revealed a main degradation pathways that was derived from the strong relationships between the applied stressor, the infrared spectra (IR) signals of the EVA encapsulant, and the amount of free acetic acid measured through TGA of EVA, indicating the hydrolysis of EVA polymer under damp heat conditions. The effect of these two mechanistic variables on the loss in \(P_{max}\) suggested a path of EVA hydrolysis driven power loss in PV modules.
Gok, Abdulkerim, David K. Ngendahimana, Cara L. Fagerholm, Roger H. French, Jiayang Sun, and Laura S. Bruckman. “Predictive Models of Poly(Ethylene-Terephthalate) Film Degradation under Multi-Factor Accelerated Weathering Exposures.” PLOS ONE 12, no. 5 (May 12, 2017): e0177614. https://doi.org/10.1371/journal.pone.0177614.
This material is based upon work supported by the Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), under Award Number DE-EE-007140.