  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.
