利用U-Net和ASPP来有效地检测光伏模块中的故障
Khalfalla Awedat1, Masoud Alajmi2, Mustafa Elfituri3
1Computer and Information Technology Department, SUNY Morrisville College, Morrisville, NY, USA. awedatk@morrisville.edu.
Scientific reports
|July 2, 2025
概括
这项研究提高了光伏 (PV) 系统故障检测使用定制的U-Net深度学习模型与修改的Atrous空间金字塔聚合 (ASPP) 模块. 该U-Net-ASPP_Hybrid模型显著提高了准确性,并减少了识别复杂热图像故障的错误.
科学领域:
- 可再生能源工程可再生能源工程
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 光伏 (PV) 系统的效率因未检测到的故障而降低,通常被复杂的热图像背景掩盖.
- 准确的故障检测对于保持太阳能基础设施的可靠性和性能至关重要.
研究的目的:
- 开发和评估新的深度学习模型,以在U-Net架构中使用定制的Atrous空间金字塔聚合 (ASPP) 来增强光伏系统的故障检测.
- 为了提高细分精度和多尺度特征提取,用于识别复杂的光伏热图像中的多种故障模式.
主要方法:
- 提出了三种修改后的U-Net-ASPP配置:U-Net-ASPP_Cent,U-Net-ASPP_Diag,以及U-Net-ASPP_Hybrid. 这三种配置包括:U-Net-ASPP_Cent,U-Net-ASPP_Diag和U-Net-ASPP_Hybrid.
- 评估模型性能,使用F1分数,交叉点在联盟 (IoU) 和损失减少等指标.
- 进行了定性细分分析,以评估故障定位能力.
主要成果:
- 与基线相比,U-Net-ASPP_Hybrid模型实现了最佳整体性能,F1得分提高了1.13%,IOU增加了3.01%,损失减少了9.86%.
- U-Net-ASPP_Cent和U-Net-ASPP_Diag显示了显著的IOU增长 (1.18%和1.96%) 并减少了虚假阳性.
- 定性分析证实了U-Net-ASPP_Hybrid模型的优越故障定位和适应性,用于现实世界的光伏检查.
结论:
- 拟议的定制U-Net-ASPP模型有效地增强了光伏故障检测,特别是复杂的热成像和特定故障模式.
- U-Net-ASPP_Hybrid模型为自动光伏检查提供了强大而适应性的解决方案,提高了系统可靠性.
- 结果表明模型的稳定性,可扩展性和在可再生能源基础设施维护中的实际应用性.
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