对于光伏模块的多式故障检测模型.
Shuaishuai Yu1, Fubao Gan2, Tao Han1
1School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, 232001, Anhui, China.
Scientific reports
|January 26, 2026
概括
本研究介绍了光伏-DETR,这是一个多式模式模型,用于使用红外和可见光图像检测光伏模块故障. 新模型提高了检测精度和效率,同时降低了计算负载.
科学领域:
- 可再生能源工程可再生能源工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 光伏行业正在迅速扩张,需要先进的故障检测方法.
- 现有的基于图像的单模模式模型在光伏模块故障检测方面存在局限性.
- 多模式方法可以提高故障检测系统的准确性和稳定性.
研究的目的:
- 开发光伏模块的多式故障检测模型.
- 克服基于图像的单模检测模型的局限性.
- 为了提高光伏模块故障检测的效率和准确性.
主要方法:
- 拟议的光伏-DETR,基于RT-DETR的多式联网故障检测模型.
- 使用ORPELAN和ReLA块模块以及辅助可逆分支构建了一个轻量级的骨干网络.
- 开发了一种重建的特征融合网络,具有注意力尺度序列融合和重组参数化.
- 使用DySample模块实现了动态上采样和下采样.
主要成果:
- 与基线RT-DETR模型相比,光伏DETR在多个数据集中证明了mAP@50%的改进.
- 实现了参数数量减少28.6%,计算负载减少28.5%.
- 在光伏模块的多式联网故障检测方面展示了出色的适应性.
结论:
- 光伏DETR在多式联网光伏模块故障检测中提供了卓越的性能.
- 该模型为工业应用提供了坚实的技术基础.
- 开发的模型提高了清洁能源基础设施的可靠性和效率.
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