Photovoltaic Panel Intelligent Management and Identification Detection System Based on YOLOv5

Photovoltaic power generation has significant energy, environmental protection and economic benefits. With the global attention to green energy, the development of photovoltaic power generation has become an inevitable trend. Photovoltaic panel assembly is a power generation device that generates direct current when exposed to sunlight, and is an important link in the photovoltaic power generation process. The geographic location of the photovoltaic panel, the user information to which the photovoltaic panel belongs, and the person in charge of the photovoltaic panel equipment are very important in the use of the photovoltaic panel, and need to be managed intelligently and efficiently. During the use of photovoltaic panels, photovoltaic panels need to undergo regular inspections to avoid affecting photovoltaic power generation output or causing safety accidents due to abnormal number and status of photovoltaic panel components. This paper builds a photovoltaic panel equipment intelligent management system to record photovoltaic equipment information in the power system. The system uses the YOLOv5 target detection model to realize image-based photovoltaic panel quantity identification and abnormality detection. The system compares with the equipment recorded information to give early warning of abnormal quantity and abnormal status. The advantage of the system proposed in this paper lies in the realization of efficient and intelligent management of photovoltaic panel information, high-precision identification of the number of photovoltaic panels, high-coverage detection of abnormal status, and real-time early warning of abnormal information.