PHOTOVOLTAIC MODULES FAULT DETECTION MODEL BASED ON YOLOv7

Zhang Wenxin; Zhou Yu; Wang Jinsong; Li Zhongyan

doi:10.19912/j.0254-0096.tynxb.2024-0540

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (8) : 333-340. DOI: 10.19912/j.0254-0096.tynxb.2024-0540

PHOTOVOLTAIC MODULES FAULT DETECTION MODEL BASED ON YOLOv7

Zhang Wenxin¹, Zhou Yu², Wang Jinsong³, Li Zhongyan^1,2

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Abstract

Aiming at the hot spot and dust fault of photovoltaic modules, this paper proposes a fault detection model based on YOLOv7. Firstly, a Data Augmentation method combining Mosaic and Mixup is introduced to expand the image dataset and enhance the model's generalization ability. Secondly, the Coordinate Attention mechanism is incorporated to effectively focus on channel features and spatial features while addressing long-range dependence issues. The experimental results show that the accuracy of the improved YOLOv7 model in detecting photovoltaic modules,dust faults and hot spot faults reaches 96.08%, 83.92% and 77.19% respectively, which is 3.66 percentage points, 1.90 percentage points and 2.27 percentage points higher than that of the original model. The mean average precision（mAP） increases from 82.48% to 83.05%. The accuracy of the model is improved and the robustness is enhanced to meet the needs of practical applications.

Key words

PV modules / object detection / deep learning / convolutional neural network / image processing

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Zhang Wenxin, Zhou Yu, Wang Jinsong, Li Zhongyan. PHOTOVOLTAIC MODULES FAULT DETECTION MODEL BASED ON YOLOv7[J]. Acta Energiae Solaris Sinica. 2025, 46(8): 333-340 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0540

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