基于YOLOv7的光伏组件故障检测模型

张文馨; 周宇; 王劲松; 李忠艳

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

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太阳能学报 ›› 2025, Vol. 46 ›› Issue (8) : 333-340. DOI: 10.19912/j.0254-0096.tynxb.2024-0540

基于YOLOv7的光伏组件故障检测模型

张文馨¹, 周宇², 王劲松³, 李忠艳^1,2

作者信息 +

PHOTOVOLTAIC MODULES FAULT DETECTION MODEL BASED ON YOLOv7

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

Author information +

文章历史 +

摘要

针对光伏组件的热斑和蒙尘故障,提出基于YOLOv7的故障检测模型。首先,引入Mosaic与Mixup结合的数据增强方法扩充图像数据集,提高模型的泛化能力;其次,引入坐标注意力机制（CA）,不仅能关注通道特征和空间特征,还能解决长程依赖的问题。实验结果表明,改进后的YOLOv7模型在检测光伏组件、蒙尘以及热斑故障时的准确率分别达到96.08%、83.92%以及77.19%,与原始模型相比分别提升3.66个百分点、1.90个百分点以及2.27个百分点;mAP值由82.48%提升至83.05%;模型精度提高,鲁棒性增强,满足实际应用需求。

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.

导出引用

张文馨, 周宇, 王劲松, 李忠艳. 基于YOLOv7的光伏组件故障检测模型[J]. 太阳能学报. 2025, 46(8): 333-340 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0540

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

中图分类号： TK514 TP391

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