Editorial Feature

Identifying Solar Panel Defects using Advanced Imaging

A team of researchers has developed new hardware and software that can detect defects in solar panels even in bright daylight, overcoming the limitations of previous systems. The novel technology streamlines the inspection process of solar panels and increases the overall efficiency of solar power by detecting defects early on.

solar panels

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Enhancing the Efficiency of Solar Panels by Identifying Defects

renewable energy sources are rapidly growing worldwide, fueled by growing pressures to switch from a reliance on fossil fuels to environmentally friendly sources to help reduce carbon emissions. In the US, like in many global regions, renewable energy is the fastest-growing energy segment over recent years. The sector grew by 100% from 2000 to 2018.

In 2018, renewable energy accounted for 17% of the total energy generation in the US with solar power making up for a considerable portion of this. Recent figures predict that solar energy will grow dramatically over the next two decades, climbing from providing 11% of the country’s renewable energy source in 2017 to 48% by 2050. Solar power is currently the fastest growing renewable energy source in the US and in the world, with it growing by 50% globally from 2019 to 2020 and predicted to grow a further 18% over 2021.

Around 90% of the world’s solar energy is generated from silicon solar panels. However, while this technology provides more energy each year, it faces a significant drawback. Defects that occur during the manufacturing, handling, or installation of the panels significantly decreases their efficiency, resulting in a reduction in power generation. These defects need to be identified quickly to retain the energy-generating capacity of the panels.

Current systems that have been established to identify such defects can only be used at night or on modules of the panels that have been removed. These methods are inefficient and time-consuming. Now, a team at Nanjing University of Science and Technology in China has developed a technique that overcomes the limitations of current methods.

In the journal Applied Optics, the scientists outline how they combined newly developed hardware together with novel software to produce a platform that can detect defects in solar panels via imaging. This process allows panels to be investigated even in bright sunlight and does not require the removal of modules.

Detecting Solar Panel Defaults in Sunlight

The key to the all-weather imaging system created by the team is the software it developed that delivers modulated electric current to a panel, forcing it to generate a short emittance of light. The team coupled this technology with an InGaAs detector with a high frame rate that can produce pictures of the solar panel while the current is being applied. These images can be used to detect faults in all lighting conditions due to the additional filter that limits the wavelengths to roughly 1150 nm, which has the effect of removing stray sunlight distorting the images.

According to the researchers, the most important part of the new technology is the new algorithm that identifies modulated parts of the image sequence from unmodulated parts and then magnifies this difference. Defects in the solar panels are demonstrated clearly in the images.

The team tested the system by applying it to two types of solar panels; those made from monocrystalline silicon and others made of polycrystalline. Evidence provided in the paper showed that the innovative system detected defects on the silicon-based solar panels with irradiances ranging from 0-1300 Watts per meter squared, equivalent to light ranging from complete darkness to sunlight.

Future Directions

The new system will revolutionize the solar energy industry as it offers a fast and simple way of detecting defaults, which will make maintenance processes far more streamlined and reduce the loss of power generation caused by defective panels. The technology will help to continue to advance the adoption of solar power across the globe as countries strive to switch to renewable energy sources in a bid to become climate neutral in line with timelines set out by the Paris Agreement.

The team aims to continue to develop its software and is currently working on reducing digital noise to further enhance its technique. The team also plans to investigate how artificial intelligence could be integrated into its system to automatically acquire images and identify defects that would further streamline the process of solar panel inspection.

In the future, we can expect further developments from this research, all of which may help to add further benefits to using solar energy. Such research projects are vital to ensuring the future sustainability of renewable energy sources which are fundamental to a carbon-neutral world.

References and Further Reading

New imaging system reveals solar panel defects even in bright sunlight. (2021) [Online]. Science Daily. Available at: https://www.sciencedaily.com/releases/2021/09/210927172915.htm

Renewables bucked the trend in 2020. (2021) [Online]. IEA. Available at: https://www.iea.org/reports/global-energy-review-2021/renewables

Renewable Energy. [Online]. Center for Climate and Energy Solutions. Available at: https://www.c2es.org/content/renewable-energy/

Wu, S., Zhang, Y., Qian, Y., Lang, Y., Yang, M. and Sun, M. (2021) Defect detection system for silicon solar panels under all-day irradiation. Applied Optics, 60(28), p.8875. https://www.osapublishing.org/ao/abstract.cfm?uri=ao-60-28-8875

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Sarah Moore

Written by

Sarah Moore

After studying Psychology and then Neuroscience, Sarah quickly found her enjoyment for researching and writing research papers; turning to a passion to connect ideas with people through writing.

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