Solar Hotspot Detection Using Thermal Image Processing in Python

Abstract

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.

Introduction

The global shift toward renewable energy has accelerated the adoption of solar photovoltaic (PV) systems, but panel efficiency can be compromised by hotspots—localized overheating caused by defects, dirt, shading, or internal faults. Hotspots reduce power output and can cause permanent damage if not detected early.

Traditional inspection methods are costly and slow, whereas thermal imaging offers a fast, non-invasive way to identify hotspots. This paper presents a Python-based automated image processing system that detects hotspots in thermal images of solar panels, highlights them with bounding boxes, and calculates the percentage of the panel area affected. This enables early maintenance and improves system efficiency.

The system’s pipeline includes image acquisition with infrared cameras, preprocessing (grayscale conversion and Gaussian blur), adaptive thresholding to segment hotspots, contour detection for localization, and damage quantification by calculating the hotspot area as a percentage of the total panel area. The results are overlaid on the original image for clear visualization.

Testing on multiple thermal images under varying conditions showed accurate detection of hotspots ranging between 10% and 30% damaged area. The system runs efficiently on typical hardware (under 2 seconds per image) and is suitable for real-time or batch processing. Limitations include sensitivity to lighting variations, potential false positives, and inability to assess hotspot severity, which future improvements may address using AI techniques.

Overall, the proposed solution is a cost-effective, efficient tool for automated solar panel inspection, with potential for integration into drone inspections and IoT-based monitoring.

Conclusion

This work presents a cost-effective, effective method for the detection of solar photovoltaic (PV) panel hotspots using thermal image processing methods adopted in Python. The system that is proposed is able to automatically inspect thermal images of solar panels, detect and locate faulty areas with abnormally high heating (hotspots), calculate the percentage of the affected area, and generate a good visual representation of findings. All these outputs are important for facilitating timely and informed maintenance interventions. Using a series of image processing operations—like image acquisition, preprocessing (grayscale conversion and noise elimination), thresholding, and contour-based detection—the technique guarantees high accuracy in the separation of areas away from normal operating temperatures. The percentage area calculated of the hotspot offers quantitative information on the extent of the problem. In addition, the system is not heavy and doesn\'t need costly hardware, thus making it a very accessible solution to multiple users ranging from single solar panel owners to business solar farm owners. The processing speed is maximized so that thermal images can be analyzed quickly, a necessity in situations where extensive solar arrays must be scanned on a regular basis. Since it has the capability of identifying issues in their early stages, the tool can considerably contribute to preventive maintenance, reduce energy losses from inefficient modules, and eventually prolong the operational lifespan of solar PV systems.

References

[1] R. Raikwar, D. Kamble, M. Kamble, N. Kamat, A. Kamble, and P. Kamble, \"Solar Hotspot Analysis using Image Processing in MATLAB,\" Int. J. for Res. in Appl. Sci. and Eng. Tech. (IJRASET), vol. 11, no. 5, pp. 722–726, May 2023. [2] A. N. N. Afifah, Indrabayu, A. Suyuti, and Syafaruddin, \"Hotspot detection in photovoltaic module using Otsu thresholding method,\" in Proc. IEEE Int. Conf. Commun., Networks and Satellite (Comnetsat), Jakarta, Indonesia, 2020, pp. 170–174. [3] K. Nitturkar, S. Vitole, M. Jadhav, and S. V. G., \"Solar panel fault detection using machine vision and image processing technique,\" in Proc. 4th Int. Conf. Trends in Electron. Informatics (ICOEI), Tirunelveli, India, 2020, pp. 1114–1118. [4] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th ed., Pearson, 2018.

Copyright

Copyright © 2025 Prof. Rajesh Raikwar , Dirghayushi Bhurase , Om Bhoye , Pravin Bolshette , Neha Bondarde , Krishna Bhutada . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.