Researchers have developed and demonstrated a new system that could detect damage to silicon solar panels in full and neutral weather conditions. Because current diagnostic methods cannot be used in daylight conditions, the new system can make it much easier to keep solar panels running smoothly.
Silicon solar panels, which make up about 90 percent of the world's solar panels, often have defects that occur during production, handling, or installation. These defects can significantly reduce the efficiency of solar panels, so it is important that they are found quickly and easily.
In the journal Optica Publishing Group Applied Optics, researchers from Nanjing University of Science and Technology in China explain how a unique combination of hardware and software allows defects in solar panels to be precisely defined and analyzed even in bright light.
"Modern disability detection systems can only be used to detect nighttime disabilities or in solar panel modules that have been removed and placed in or in a shady place," said Yunsheng Qian, who led the research team. "We hope that this program can be used to help inspectors in photovoltaic power stations to identify defects and identify them more quickly so that these systems can generate electricity at higher levels."
In a new experiment, researchers created a global climate reflection system that works in any lighting conditions. To make the bugs visible, they built software that uses a limited amount of electrical energy on the solar panel, which causes it to emit a flashing light and work very quickly. An InGaAs detector with a very high frame rate is used to detect the sequence of images of solar panels as electric power is used. Investigators also added a filter measuring the length of the wavelength obtained to an estimated 1150 nm to extract the missing sunlight from the images.
Investigators are now working on software to help reduce digital noise to improve image quality, so that detector can collect image changes more accurately. They also want to look at whether artificial intelligence can be applied to images obtained to automatically identify types of errors and further the testing process.
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