Novel Cloud-Edge Collaborative Detection Technique for Detecting Defects in PV Components, Based on Transfer Learning

Hongxi Wang, Fei Li, Wenhao Mo, Peng Tao, Hongtao Shen, Yidi Wu, Yushuai Zhang and Fangming Deng ()
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Hongxi Wang: Marketing Service Center, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050035, China
Fei Li: Marketing Service Center, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050035, China
Wenhao Mo: China Electric Power Research Institute, Beijing 100192, China
Peng Tao: Marketing Service Center, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050035, China
Hongtao Shen: Marketing Service Center, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050035, China
Yidi Wu: State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050021, China
Yushuai Zhang: Marketing Service Center, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050035, China
Fangming Deng: School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China

Energies, 2022, vol. 15, issue 21, 1-16

Abstract: The existing techniques for detecting defects in photovoltaic (PV) components have some drawbacks, such as few samples, low detection accuracy, and poor real-time performance. This paper presents a cloud-edge collaborative technique for detecting the defects in PV components, based on transfer learning. The proposed cloud model is based on the YOLO v3-tiny algorithm. To increase the detection effect of small targets, we produced a third prediction layer by fusing the shallow feature information with the stitching layer in the second detection scale and introducing a residual module to achieve improvement of the YOLO v3-tiny algorithm. In order to further increase the ability of the network model to extract target features, the residual module was introduced in the YOLO v3-tiny backbone network to increase network depth and learning ability. Finally, through the model’s transfer learning and edge collaboration, the adaptability of the defect-detection algorithm to personalized applications and real-time defect detection was enhanced. The experimental results showed that the average accuracy and recall rates of the improved YOLO v3-tiny for detecting defects in PV components were 95.5% and 93.7%, respectively. The time-consumption of single panoramic image detection is 6.3 ms, whereas the consumption of the model’s memory is 64 MB. After cloud-edge learning migration, the training time for a local sample model was improved by 66%, and the accuracy reached 99.78%.

Keywords: cloud-edge collaboration; defect recognition; transfer learning (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: 2022
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