Solar Hotspot Detection Using Thermal Image Processing in Python

The growing use of solar panels calls for effective maintenance to yield maximum performance. This paper offers a computer vision-based image processing platform for the detection and analysis of thermal hotspots in solar panels from thermal images. The procedure starts with image acquisition of the thermal image, grayscale conversion, and denoising as image preprocessing steps. Thresholding is carried out to identify important pixel intensities to detect hotspots accurately. Hotspots thus identified are then measured to arrive at defect areas and are displayed graphically for easier interpretation. Experimental findings show the system effectively identifying and segregating hotspot areas, with the resultant processed output totaling four hotspots. The area of an average hotspot was found to be 16,885.25 pixels, with the largest and smallest hotspots having 66,431.00 and 116.00 pixels respectively. The total solar panel damage was calculated to be 21.30%. This approach enables fault detection at an early stage and can be used to support predictive maintenance planning for photovoltaic systems.