Variability and associated uncertainty in image analysis for soiling characterization in solar energy systems

Journal article


Greg Smestad, Cody B. Anderson, Michael E. Cholette, Pavan Fuke, Ahmed Amine Hachicha, Anil Kottantharayil, Klemens Ilse, Mounia Karim, Muhammad Zahid Khan, Herbert Merkle, David Miller, Jimmy M. Newkirk, Giovanni Picotti, Florian Wiesinger, Guido Willers and Leonardo Micheli 2023. Variability and associated uncertainty in image analysis for soiling characterization in solar energy systems. Solar Energy Materials and Solar Cells. 259, pp. 1-15. https://doi.org/10.1016/j.solmat.2023.112437
AuthorsGreg Smestad, Cody B. Anderson, Michael E. Cholette, Pavan Fuke, Ahmed Amine Hachicha, Anil Kottantharayil, Klemens Ilse, Mounia Karim, Muhammad Zahid Khan, Herbert Merkle, David Miller, Jimmy M. Newkirk, Giovanni Picotti, Florian Wiesinger, Guido Willers and Leonardo Micheli
Abstract

The accumulation of soiling on photovoltaic modules and on the mirrors of concentrating solar power systems causes non-negligible energy losses with economic consequences. These challenges can be mitigated, or even prevented, through appropriate actions if the magnitude of soiling is known. Particle counting analysis is a common procedure to characterize soiling, as it can be easily performed on micrographs of glass coupons or solar devices that have been exposed to the environment. Particle counting does not, however, yield invariant results across institutions. The particle size distribution analysis is affected by the operator of the image analysis software and the methodology utilized. The results of a round-robin study are presented in this work to explore and elucidate the uncertainty related to particle counting and its effect on the characterization of the soiling of glass surfaces used in solar energy conversion systems. An international group of soiling experts analysed the same 8 micrographs using the same open-source ImageJ software package. The variation in the particle analyses results were investigated to identify specimen characteristics with the lowest coefficient of variation (CV) and the least uncertainty among the various operators. The mean particle diameter showed the lowest CV among the investigated characteristics, whereas the number of particles exhibited the largest CV. Additional parameters, such as the fractional area coverage by particles and parameters related to the distribution's shape yielded intermediate CV values. These results can provide insights on the magnitude inter-lab variability and uncertainty for optical and microscope-based soiling monitoring and characterization.

Keywordssolar power systems; photovoltaic modules; soiling monitoring and characterization
Year2023
JournalSolar Energy Materials and Solar Cells
Journal citation259, pp. 1-15
PublisherElsevier BV
ISSN0927-0248
Digital Object Identifier (DOI)https://doi.org/10.1016/j.solmat.2023.112437
Web address (URL)https://www.sciencedirect.com/science/article/pii/S0927024823002581
Output statusPublished
Publication dates04 Jul 2023
Publication process dates
Accepted16 Jun 2023
Deposited24 Aug 2023
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https://repository.derby.ac.uk/item/q03x0/variability-and-associated-uncertainty-in-image-analysis-for-soiling-characterization-in-solar-energy-systems

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