Solar Photovoltaic (PV) is increasingly being used to address the global concern of energy security. However, hot spot and snail trails in PV modules caused mostly by crakes reduce their efficiency and power capacity. This article presents a groundbreaking methodology for automatically identifying and analyzing anomalies like hot spots and snail trails in Solar Photovoltaic (PV) modules, leveraging unsupervised sensing algorithms and 3D Augmented Reality (AR) visualization. By transforming the traditional methods of diagnosis and repair, our approach not only enhances efficiency but also substantially cuts down the cost of PV system maintenance. Validated through computer simulations and real-world image datasets, the proposed framework accurately identifies dirty regions, emphasizing the critical role of regular maintenance in optimizing the power capacity of solar PV modules. Our immediate objective is to leverage drone technology for real-time, automatic solar panel detection, significantly boosting the efficacy of PV maintenance. The proposed methodology could revolutionize solar PV maintenance, enabling swift, precise anomaly detection without human intervention. This could result in significant cost savings, heightened energy production, and improved overall performance of solar PV systems. Moreover, the novel combination of unsupervised sensing algorithms with 3D AR visualization heralds new opportunities for further research and development in solar PV maintenance.

翻译：太阳能光伏（PV）正日益被用于应对全球能源安全问题。然而，主要由裂纹引起的光伏组件热点和蜗牛纹会降低其效率和功率容量。本文提出一种突破性方法，利用无监督感知算法和三维增强现实（AR）可视化技术，自动识别并分析太阳能光伏组件中的热点和蜗牛纹等异常。通过变革传统的诊断与修复方法，本方法不仅提升了效率，还显著降低了光伏系统维护成本。经计算机仿真和真实图像数据集验证，所提框架能准确识别污损区域，凸显了定期维护在优化光伏组件功率容量中的关键作用。我们的近期目标是利用无人机技术实现实时自动太阳能电池板检测，大幅提升光伏维护效能。该研究成果有望革新光伏维护领域，实现无需人工干预的快速精准异常检测。这将带来显著成本节约、能源产量提升及光伏系统整体性能改善。此外，无监督感知算法与三维AR可视化的创新组合，为光伏维护领域的进一步研究与发展开创了新机遇。