Latest news with #OmanScienceFestival
Times of Oman
06-03-2025
- Science
- Times of Oman
Omani students develop innovative solar panel defect detection system
Muscat: A student team from the University of Technology and Applied Sciences in Shinas has created an innovative technique for detecting defects in solar panels using a drone equipped with thermal imaging. This technology enables rapid defect analysis and resolution, earning the team numerous local and regional awards, as well as presentations at various scientific conferences and events. Fatma bint Mohammed Al-Maamari, a team member, explained that the project addresses defects caused by environmental, electrical, and mechanical stress. The drone, carrying a thermal camera, gathers data which is then processed using advanced technologies, including a "Deep Learning Mod." The project progressed through research, preparation, data collection, and evaluation stages. The team focused on improving defect detection accuracy and reducing processing time, categorizing solar panel images as intact or defective, and enhancing them with contrast and filtering techniques. Mahra bint Saeed Al-Kaabi, another team member, noted that the prototype was tested on solar panels at the university. Practical experiments and adjustments were conducted to refine the model. The team then deployed the model in a real-world environment to monitor solar panel performance periodically, documenting all stages and results in a detailed report. Zahra bint Saleh Al-Sanani highlighted the team's ongoing work to integrate drones with augmented reality and advanced artificial intelligence applications to further enhance defect detection accuracy. They are also developing 3D models for better defect visualization and automated processes for defect detection and maintenance scheduling. Sara bint Saeed Al-Saidi emphasized the project's significance, noting its publication in prestigious international scientific journals, including "Energy," "Hydrogen Energy," and "IEEE Xplore." The project has been presented at events such as the Oman Science Festival, the Second International Conference on Computing and Data Analysis, the Sixth International Gas Conference at Qatar University, and the Fifteenth International Conference on Hydropower Production at the University of Sharjah in the UAE. The team earned third place in the Royal Army of Oman's Scientific Club competition for the best innovation and received the Research Excellence Award at Majan College.
Muscat Daily
05-03-2025
- Science
- Muscat Daily
Omani students develop drones to detect solar panel defects
Muscat – A group of students from the University of Technology and Applied Sciences (UTAS) in Shinas has developed a drone-based system to detect defects in solar panels, enhancing the efficiency of solar energy systems. The innovation integrates thermal imaging and deep learning to identify defects caused by environmental, electrical, and mechanical stresses. It aims to improve detection accuracy while reducing maintenance time. Led by Fatima Mohammed al Maamari, the team received multiple awards at local and regional levels. The project has been recognised at several scientific conferences and exhibitions. The system uses a thermal camera to scan solar panels and artificial intelligence to analyse irregularities. Advanced technologies such as augmented reality and 3D modelling have been incorporated to refine the detection process. Team member Zahraa bint Saleh al Sanani said, 'We're working on integrating augmented reality with drones to provide a more accurate and efficient defect visualisation system.' The project has been featured in international scientific journals, including Energy and IEEE Xplore, and presented at events such as the Oman Science Festival, the International Gas Conference at Qatar University, and the Hydroelectric Power Conference at the University of Sharjah. With its practical applications already demonstrated, the technology has the potential to contribute significantly to the advancement of solar energy in Oman and beyond.
