Abstract:
The project presented in this final year aims to develop an autonomous solar panel maintenance system that will make the process efficient and reliable through the integration of drone technology, deep learning, and advanced image processing etc. Fitted with high-definition cameras and the thermal imaging device, drones monitor the inspections in real-time to determine possible problems like accumulation of smoke and other debris, physical harm, and electrical malfunctions. Deep learning model trains the data captured and predicts faults as well as maintaining them in time through a customized deep learning model. In order to have constant monitoring of the performance, the system applies object tracking and image segmentation feature so that it is able to monitor the gradual change in the conditions of the panel. After an issue is detected, the system chooses the best technique of maintenance, so it may be automated cleaning with water jets or rotating brushes or air blowers. The system will automate inspections and maintenance thus cutting manual work, decreasing running costs and ensuring that solar panels remain at optimal efficiency.Maintenance of traditionally structured solar panels can be not only messy but also unreliable and causes degrades in performance with time. The solution eliminates this problem by providing proactive and smarter monitoring that uses realtime automated monitoring. The machine ensures solar panel effectiveness at top levels and eliminates any possibilities of the degradation of its abilities long-term without much intervention of human beings.
