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RFE-YOLO:基于多式特征融合的光伏模块故障检测算法的研究
Yuyang Guo1,2, Xiuling Wang1,2, Zhichao Lin1,2
1College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了一种RFE-YOLO模型,用于使用多式联网数据增强光伏 (PV) 故障检测. 该模型提高了智能光伏电站运行和维护的精度和准确性.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 可再生能源系统可再生能源系统
背景情况:
- 光伏 (PV) 模块的运行状态对于发电效率至关重要.
- 准确的故障检测对于光伏发电厂的智能运行和维护 (O&M) 是必不可少的.
- 单模态图像分析在复杂的光伏环境中面临局限性.
研究的目的:
- 开发一个多式联网数据集和一个新的深度学习模型,用于精确的光伏模块故障检测.
- 解决光伏系统中单模态图像分析的感知局限性.
- 增强集中式光伏发电厂的智能运营和管理.
主要方法:
- 为光伏发电厂构建RGBIRPV多式联网数据集.
- 关于RFE-YOLO模型的建议,该模型包含RC,FA和EVG模块,用于增强特征提取和融合.
- 利用基于CBAM的注意力机制和GSConv进行高效的多式联络数据处理.
主要成果:
- RFE-YOLO模型在YOLOv11n上表现出显著的改进,精度提高了2.9%,mAP@50提高了1.8%,F1得分提高了1.5%.
- 该模型通过差异化功能增强和自适应融合有效地集成可见和红外数据.
- 实现了浅层细节和深层语义的协同表示,计算成本低.
结论:
- RFE-YOLO模型提供了一种有效的技术解决方案,用于在现实条件下快速准确地检测光伏模块故障.
- 开发的多式联网数据集和模型增强了光伏发电厂的智能O&M能力.
- 这种方法克服了单模分析对提高光伏系统性能的局限性.
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