Modelling photovoltaic soiling losses through optical characterization
Description
Data and additional plots for the Scientific Reports paper titled, "Modelling photovoltaic soiling losses through optical characterization". This includes the spectral transmittance data and all of the optical microscopy. The manuscript presents the results of an international collaboration that investigated the spectral effects of soiling and dust naturally deposited on PV glass exposed outside at seven locations worldwide. These places were chosen to represent a wide variety of climates and environmental conditions. They are also relevant to the continued growth of installed PV systems. We report on one of the largest pools of naturally deposited soiling on glass. While most of the published soiling work focuses on a single site, there are few comparative studies of soiling from multiple locations. The glass coupons were analyzed after an 8-week outdoor exposure, but were not meant to reflect a complete picture of soiling at a given site. Our focus was on the fundamental aspects of soiling from the perspective of both the spectral shape of the transmittance and the corresponding particle size distribution. The optical transmittance and the particle size distribution of the soiling were compared in order to find correlations that could be universally valid, and that could open possibilities to modelling PV soiling losses. Together with this, we present the first effort where empirical models from other disciplines are applied in order to describe both the spectral characteristics of the soiled samples and their corresponding particle size distributions. The spectral hemispherical transmittance of the soiled coupons was taken using a Cary 500 spectrophotometer. The transmittance was fit to a modified form of the Ångström turbidity equation. Microscope images were captured at 100x and 500x magnification for the coupons soiled at each of the seven sites. Micrographs for each glass coupon were taken using a Keyence VHX-5000 microscope at a resolution of 1200 pixels x 800 pixels. The micrographs were analyzed with ImageJ to obtain the particle size distribution.
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Transmission measurements were made with an integrating sphere attachment for the spectrophotometer, and at 1 nm steps between 300 nm and 1100 nm. For the processing of the broadband transmittance, only the hemispherical transmittance between 350 nm and 1100 nm was considered, because of the confounding factor of the absorption of the glass itself at wavelengths less than 350 nm. The transmittance of a clean reference coupon was taken at the start and the end of each set of measurements to check the consistency of the measurement and to obtain the relative hemispherical transmittance of soiling, calculated as a ratio of the hemispherical transmittance of each soiled coupon to the hemispherical transmittance of the unsoiled reference coupon. Three measurements were taken per coupon and averaged. Then, three more measurements were taken in a different location on the coupon and averaged. This procedure was done to partially mitigate the non-uniformity of soiling. Only one set of transmittance measurements (i.e., one spot) was available for Chennai and Jaén. We then applied an offset correction to all the measurements to correct for detector and filter change near 800 nm. The offset was calculated as the difference between the broadband transmittance between 790 nm and 799 nm and the broadband transmittance between 800 nm and 809 nm. Then, the offset correction to all the data for wavelengths λ≥ 800 nm was applied. Although a full uncertainty analysis has not been completed for the spectrophotometer work, the standard uncertainty associated with the repeatability of the transmittance measurements is estimated to be ±0.005. The curve fitting of the corrected transmittance was then made to a modified form of the Ångström turbidity equation. This was performed through the curve_fit function in the SciPy library for Python 2.7 [E. Jones, E. Oliphant, P. Peterson, et al., SciPy: Open Source Scientific Tools for Python, (2001), http://www.scipy.org/]. For the optical microscopy, the image settings (brightness, contrast, texture, color, and lighting) were adjusted to capture images under optimal conditions. The micrographs were then analyzed using the image processing software package, ImageJ [ImageJ Particle Analysis: Automatic Particle Counting, https://imagej.net/Particle_Analysis]