A research team from China’s Northeast Agricultural University and Shenzhen University has developed a real-time shadow detection method for PV modules. The team has published an article in the scientific journal Nature, titled ‘The real-time shadow detection of the PV module by computer vision based on histogram matching and gamma transformation method.’ The method addresses the common issue of shading caused by external factors like leaves and buildings, which reduce solar panel efficiency and create safety risks. Using computer vision, the technique combines gamma transformation and histogram matching to enhance critical image features, followed by gray-level slicing to detect shadows in real time. Testing revealed high detection accuracy (0.98) and fast processing times (0.721 seconds per frame), significantly outperforming traditional methods such as Canny detection and Random Forest. This low-cost, highly efficient solution improves PV module monitoring and helps optimize solar energy output, says the research team.
Delft University researchers propose integrating inductors into PV modules to improve shade tolerance using MPPT. (Image Source: ScienceDirect)
Fraunhofer’s MaNiTU project developed perovskite-silicon tandem solar cells with 31.6% efficiency using scalable production methods. (Image Source: Fraunhofer ISE)
Through advanced passivation techniques, JA Solar’s Bycium+ cell has achieved a record-breaking open-circuit voltage of 748.6 mV. (Image Source: JA Solar)
To tackle challenges and advance AI-driven maintenance for a net-zero solar future, as revealed by Fluke’s survey. (Image Credit/Source: PublicDomainPictures/Pixabay)