Foreign Object Shading Detection in Photovoltaic Modules Based on Transfer Learning
Bin Liu,
Qingda Kong,
Hongyu Zhu (hongyuzhu@st.gxu.edu.cn),
Dongdong Zhang,
Hui Hwang Goh and
Thomas Wu
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Bin Liu: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Qingda Kong: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Hongyu Zhu: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Dongdong Zhang: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Hui Hwang Goh: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Thomas Wu: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Energies, 2023, vol. 16, issue 7, 1-14
Abstract:
As a representative new energy source, solar energy has the advantages of easy access to resources and low pollution. However, due to the uncertainty of the external environment, photovoltaic (PV) modules that collect solar energy are often covered by foreign objects in the environment such as leaves and bird droppings, resulting in a decrease in photoelectric conversion efficiency, power losses, and even the “hot spot” phenomenon, resulting in damage to the modules. Existing methods mostly inspect foreign objects manually, which not only incurs high labor costs but also hinders real-time monitoring. To address these problems, this paper proposes an IDETR deep learning target detection model based on Deformable DETR combined with transfer learning and a convolutional block attention module, which can identify foreign object shading on the surfaces of PV modules in actual operating environments. This study contributes to the optimal operation and maintenance of PV systems. In addition, this paper collects data in the field and constructs a dataset of foreign objects of PV modules. The results show that the advanced model can significantly improve the target detection AP values.
Keywords: photovoltaic module; foreign object shading detection; transfer learning; convolutional block attention module (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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