The real-time shadow detection of the PV module by computer vision based on histogram matching and gamma transformation method

• Published in: Scientific reports 2024-09, Vol.14 (1), p.21781-17, Article 21781
• Main Authors: Liu, Xinyi, Xia, Haonan, Li, Ke, Lu, Yinghui, Lv, Shanshan, Zhao, Qinghe, Song, Weixian, Wang, Lishu
• Format: Article
• Language: English
• Subjects: 639/166/987; 639/4077/909/4101/4096; Alternative energy sources; Computer vision; Digital image processing; Environmental monitoring; Humanities and Social Sciences; Image processing; Monitoring methods; multidisciplinary; Photovoltaic module; Photovoltaics; Real-time; Renewable energy; Science; Science (multidisciplinary); Shading; Shadow detection; Solar energy
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-71710-x

Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance.