Integrating UAV Imagery and Hybrid Metrics for PV Degradation Analysis: Insights from a 13-Year Field Study in Dhahran, KSA

This study offers an extensive analysis of the prolonged deterioration of photovoltaic (PV) panels exposed to the harsh desert climate in Dhahran, Saudi Arabia, over a span of 13 years. Preliminary laboratory investigations performed at the Gulf Renewable Energy Laboratory (GRL) established essential baseline performance criteria for comparative evaluation. Panels exposed in the field demonstrated a degradation rate ranging from 45% to 60% compared to fresh, unexposed panels. To augment these findings, high-resolution aerial imagery was obtained using a DJI Mavic Air 2 drone, subsequently employing sophisticated image processing techniques - specifically histogram equalisation, Gaussian blurring, and Canny edge detection - to detect and illustrate surface imperfections such as microcracks and erosion. A unique hybrid technique for quantifying degradation, integrating the Structural Similarity Index (SSIM) and Pixel Intensity Difference (PID), was introduced, producing degradation estimates between 55% and 75%. Image-based assessments yielded results that closely corresponded with GRL data, attaining an estimated diagnostic accuracy of 90%. These data validate that increased temperatures and UV exposure lead to polymer breakdown, encapsulant discolouration, and optical losses caused by dust. The use of drone-acquired imagery and image-based analytics presents a cost-efficient, scalable substitute for conventional laboratory diagnostics. This study emphasises the essential function of remote sensing and hybrid visual inspection in improving the oversight, upkeep, and sustainability of solar energy infrastructure in dry regions.