用无人机红外成像检测光伏电站缺陷，提升大规模运维效率。
Aerial Infrared Health Monitoring of Solar Photovoltaic Farms at Scale
- 基于高空热成像数据，用机器学习定位肉眼不可见的光伏板故障。
- 构建了覆盖数千个站点的地理多样数据集，支持跨区域缺陷识别。
- 适合能源公司、智能运维团队及可再生能源研究者参考。
光伏发电农场是全球可再生能源的重要来源，但其实际运行效率在大规模下仍难以掌握。本文提出一种全面的数据驱动框架，用于对北美洲太阳能设施进行大规模空中红外检测。利用高分辨率热成像，构建并整理了一个地理分布广泛的数据库，涵盖数千个光伏站点，支持基于机器学习的缺陷检测与定位，这些缺陷在可见光谱中无法被发现。我们的流程整合了先进的图像处理、地理配准和空中热红外异常检测技术，提供性能损失的严谨估算。文章还强调了空中数据采集、标注方法及模型部署在多种环境与运行条件下的实际考量。研究揭示了大型光伏资产的可靠性新见解，并为性能趋势分析、预测性维护和可扩展数据分析提供了基础。
原文摘要 · Abstract (English)
Solar photovoltaic (PV) farms represent a major source of global renewable energy generation, yet their true operational efficiency often remains unknown at scale. In this paper, we present a comprehensive, data-driven framework for large-scale airborne infrared inspection of North American solar installations. Leveraging high-resolution thermal imagery, we construct and curate a geographically diverse dataset encompassing thousands of PV sites, enabling machine learning-based detection and localization of defects that are not detectable in the visible spectrum. Our pipeline integrates advanced image processing, georeferencing, and airborne thermal infrared anomaly detection to provide rigorous estimates of performance losses. We highlight practical considerations in aerial data collection, annotation methodologies, and model deployment across a wide range of environmental and operational conditions. Our work delivers new insights into the reliability of large-scale solar assets and serves as a foundation for ongoing research on performance trends, predictive maintenance, and scalable analytics in the renewable energy sector.
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