Scientists in Oman have analyzed the effect of soiling, cleaning, and water injection on the performance of PV panels in Oman. They have found the use of water for cooling may increase power yield by up to 23.9%.
Laboratory and outdoor soiling experiments conducted in Saudi Arabia have shown that increased particle resuspension by wind is one of the dominant factors for high anti-soiling performance in photovoltaic glass.
Researchers from China and the UK have improved the Adam optimization algorithm to achieve better results in dust detection on PV panels. The optimized algorithm reportedly performed better than most common algorithms used for dust detection.
New research from Pakistan shows that dust could reduce PV panel performance through the shielding effect and the “dust-temperature” phenomenon. The scientists tested two PV systems in different parts of the country.
Thresholding methods have commonly been used to characterize the soiling accumulated on glass coupons. Researchers led by the Sapienza University of Rome have identified 16 automatic thresholding methods that may be used for analyzing soiling on PV panels.
Scientists in India have developed a novel way to predict soiling accumulation on bifacial modules. Their approach considers dust deposition, rebound, and resuspension phenomena.
A European group has looked into the soiling impact on PV modules in Oman. They have collected 60 samples, based on season, month and tilt angles.
Researchers in Oman have investigated the effects of soiling on solar module performance and have found that between 8 and 12 cleaning cycles may be enough to ensure higher energy yields.
A group of scientists in the United States saw ‘encouraging’ results after testing the commercialization of novel coating materials in field tests, with the coating only increasing a panel’s total cost by 1.4%.
Scientists in Cyprus evaluated six different models used to predict the power losses caused by the accumulation of dust, dirt, and other substances on the surface of PV panels in the island’s arid climate. Results from the various models were compared with soiling loss data from a “test bench” installation at the University of Cyprus in Nicosia, revealing a potential advantage for machine-learning approaches backed by satellite data.
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