DETERMINATION OF PROBABLE PLACES OF DAMAGE TO PV MODULES

Abstract

The article is devoted to the automation of photovoltaic system diagnostics processes, which reduces time and resources. Photovoltaic systems are usually diagnosed using traditional methods such as visual inspection or thermography. However, these methods have significant disadvantages: high cost due to the significant time required. The article focuses on the use of unmanned automated systems, in particular quadcopters, to improve diagnostic efficiency. These technologies make it possible to automate the survey processes, including object inspection, data collection and subsequent analysis. At the same time, it is noted that the survey itself is only the initial stage of work, while the analysis of the data remains the most complex and time-consuming part of the process.

The authors investigate the optimisation of key stages of analysis, which can significantly reduce time and resources, but at the same time affects a number of technical aspects of the flight, such as the trajectory of the drones, battery consumption and duration of operation. Particular attention is paid to the correlation between flight efficiency and data collection quality, as well as the impact of these factors on the accuracy of diagnostic methods. The article also discusses the legal and technical challenges associated with the use of drones, including the need for certified pilots and compliance with safety regulations.

The practical application of the discussed automation principles to specific examples, such as solar power plants, demonstrates the possibility of a significant reduction in diagnostic time and operational costs compared to traditional methods. The results indicate that automation of photovoltaic system diagnostics is a promising area that combines innovative technologies and economic efficiency.