Automated complex of intelligent monitoring of a solar power plant

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Abstract
A solution for automated monitoring and diagnostics of photovoltaic modules of industrial solar power plants is proposed. The solution is based on the use of an unmanned aerial vehicle with a specialized payload and a ground-based intelligent information and control system to detect problem areas of the station, in particular partial shading and pollution. To perform the detection procedures, a neural network based on the Fast R-CNN architecture with the learning algorithm – Inception v2 (COCO) was used. The results of preliminary tests showed that the accuracy of detecting problem areas is at least 92%. The article presents a mathematical model that allows calculating the installed power monitored by the complex, depending on the type of station and UAV, meteorological parameters, and the performance of computing equipment. Numerical calculations have shown that when using the FIMI X8SE UAV and a computing device based on the RTX2080 GPU, the installed monitored power will be up to 7.5 MW.

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