Shinas University students innovate solar panel defect detection technology
Published: 06:03 PM,Mar 08,2025 | EDITED : 10:03 PM,Mar 08,2025
Students from UTAS, Shinas have developed an innovative drone-based tech to detect defects in energy-generating solar panels
SHINAS: A team of students from the University of Technology and Applied Sciences in Shinas has successfully developed an innovative drone-based technology to detect defects in energy-generating solar panels.
This cutting-edge invention utilises advanced analytical techniques to identify and address defects swiftly, enhancing the efficiency and reliability of solar energy systems. The project has received multiple awards at both local and Gulf levels and has been showcased at various scientific conferences and events.
Fatima bint Mohammed Al Maamari, a member of the student team, explained that the project aims to detect defects in solar modules caused by environmental, electrical, and mechanical stress. The technology employs a drone equipped with a thermal imaging camera to capture data, which is then analysed using advanced techniques such as the 'Deep Learning Mod' technology.
She further elaborated on the development process, highlighting the various stages of the project. Initially, the team conducted extensive research, reviewing previous studies and refining their objectives.
Key goals included enhancing defect detection accuracy and reducing processing time. The next stage involved data collection, where images of solar units were gathered from diverse sources and categorised as either healthy or defective. These images were then processed through contrast and filtering technologies to improve clarity and accuracy. During the evaluation phase, model parameters were fine-tuned to optimise performance.
Student Mahra bint Saeed Al Kaabi, another team member, detailed the practical testing of the project.
The initial prototype was implemented on solar panels within the university, undergoing rigorous testing and refinements based on real-world applications. Necessary modifications were made to enhance functionality, culminating in a final model that was successfully deployed in a real environment, demonstrating high performance and reliability. - ONA
This cutting-edge invention utilises advanced analytical techniques to identify and address defects swiftly, enhancing the efficiency and reliability of solar energy systems. The project has received multiple awards at both local and Gulf levels and has been showcased at various scientific conferences and events.
Fatima bint Mohammed Al Maamari, a member of the student team, explained that the project aims to detect defects in solar modules caused by environmental, electrical, and mechanical stress. The technology employs a drone equipped with a thermal imaging camera to capture data, which is then analysed using advanced techniques such as the 'Deep Learning Mod' technology.
She further elaborated on the development process, highlighting the various stages of the project. Initially, the team conducted extensive research, reviewing previous studies and refining their objectives.
Key goals included enhancing defect detection accuracy and reducing processing time. The next stage involved data collection, where images of solar units were gathered from diverse sources and categorised as either healthy or defective. These images were then processed through contrast and filtering technologies to improve clarity and accuracy. During the evaluation phase, model parameters were fine-tuned to optimise performance.
Student Mahra bint Saeed Al Kaabi, another team member, detailed the practical testing of the project.
The initial prototype was implemented on solar panels within the university, undergoing rigorous testing and refinements based on real-world applications. Necessary modifications were made to enhance functionality, culminating in a final model that was successfully deployed in a real environment, demonstrating high performance and reliability. - ONA