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. 2020 Jan 9;10(1):58.
doi: 10.1038/s41598-019-56868-z.

Modelling photovoltaic soiling losses through optical characterization

Affiliations

Modelling photovoltaic soiling losses through optical characterization

Greg P Smestad et al. Sci Rep. .

Abstract

The accumulation of soiling on photovoltaic (PV) modules affects PV systems worldwide. Soiling consists of mineral dust, soot particles, aerosols, pollen, fungi and/or other contaminants that deposit on the surface of PV modules. Soiling absorbs, scatters, and reflects a fraction of the incoming sunlight, reducing the intensity that reaches the active part of the solar cell. Here, we report on the comparison of naturally accumulated soiling on coupons of PV glass soiled at seven locations worldwide. The spectral hemispherical transmittance was measured. It was found that natural soiling disproportionately impacts the blue and ultraviolet (UV) portions of the spectrum compared to the visible and infrared (IR). Also, the general shape of the transmittance spectra was similar at all the studied sites and could adequately be described by a modified form of the Ångström turbidity equation. In addition, the distribution of particles sizes was found to follow the IEST-STD-CC 1246E cleanliness standard. The fractional coverage of the glass surface by particles could be determined directly or indirectly and, as expected, has a linear correlation with the transmittance. It thus becomes feasible to estimate the optical consequences of the soiling of PV modules from the particle size distribution and the cleanliness value.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Effect of soiling on the transmittance and reflectance of light incident on glass. Diagram courtesy of Al Hicks/NREL and used by permission.
Figure 2
Figure 2
Transmittance vs. wavelength (350–1100 nm) curves for glass coupons soiled at two representative locations, (left) Chennai, India, and (right) San José, California. Also shown is the fit to the modified Ångström equations, (red) Eq. (2) and (green) Eq. (3). The measurements are referenced to clean glass.
Figure 3
Figure 3
Broadband relative transmittance τb (350–1100 nm) as a function of γ* (left-plot) and βsur (right-plot) for the seven study sites. The location is indicated by the color. All the sites except Jaén and Chennai had two spots measured on the coupon, and both were plotted separately.
Figure 4
Figure 4
Particle size distribution density, dN(D)/dD, left y-axis, and cumulative fractional area coverage, on the right y-axis, both estimated by ImageJ using the 100× optical micrographs. Two representative locations are shown: (left) Chennai and (right) San José. The bin size is 1 µm.
Figure 5
Figure 5
Cumulative particle size distribution and best fit (to IEST-STD-CC 1246E) at 100× for two representative locations.
Figure 6
Figure 6
The fractional area coverage of the deposited particles measured directly using ImageJ (vertical axis) versus that estimated from Eq. (6) (horizontal axis). For this graph, all sites except Egypt were considered and only data obtained using the 100× magnification was utilized. The markers are colored according to the RMSE found in fitting Eq. (5).
Figure 7
Figure 7
Broadband relative transmittance losses 1 − τb versus measured fractional area coverage f for all sites except for Egypt. The wavelength range for the transmittance was 350–1100 nm, while f is obtained from the 100× images using ImageJ. The markers are colored according to the RMSE found in fitting Eq. (5).

References

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