Unmanned aerial vehicle photovoltaic fault diagnosis and positioning method based on attention neural network

Abstract

The invention relates to an unmanned aerial vehicle photovoltaic fault diagnosis and positioning method based on an attention neural network, and the method comprises the steps: 1, obtaining an infrared image of a photovoltaic module aerially photographed by an inspection unmanned aerial vehicle, and reading the real-time position information and attitude data of the unmanned aerial vehicle; 2, constructing an FPT structure based on the attention mechanism and the FPN structure; 3, constructing a BP neural network, carrying out information fusion by adopting an FPT structure, taking aerial pictures as input, taking pixel coordinates of the photovoltaic module where the defects are located as output, and training the constructed neural network to obtain a neural network for infrared image defect detection; 4, segmenting the original image to obtain a photovoltaic module mask, determining a faulty photovoltaic module according to a positioning result of the target detection network, and obtaining corner pixel coordinates of a target module; and 5, constructing a coordinate conversion model according to the real-time coordinates shot by the unmanned aerial vehicle and the attitude angle, converting the pixel coordinates output by the neural network into position coordinates under geodetic coordinates according to the ground-air geometrical relationship, and obtaining the position information of the photovoltaic module where the defect is located. The method is suitable for photovoltaic module defect detection and positioning based on unmanned aerial vehicle inspection, can realize real-time monitoring of module defects, and improves the defect detection precision.

Description

Technical field [0001] The present invention belongs to the field of intelligent inspection of solar photovoltaic power station faults, specifically based on the attention neural network of unmanned aerial vehicle photovoltaic fault diagnosis and positioning method. Background [0002] With the increase in social demand for green and clean energy, the photovoltaic power plant industry based on solar power generation technology has developed rapidly. Photovoltaic power station covers a large area, mainly distributed in desert, wasteland, water surface and other wild natural environment, photovoltaic modules placed in a harsh outdoor environment, perennial wind and sun, resulting in serious failures and defects they face. Real-time control and testing and daily maintenance of power generation systems often require high labor costs, and there are drawbacks such as strong subjectivity and single inspection means, which is difficult to meet the growing demand for inspection. In order ...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the above technical problems, the present invention proposes a UAV photovoltaic fault diagnosis and positioning method based on the attention neural network, which alleviates the problem of insufficient positioning accuracy of deep learning target detection and is suitable for photovoltaic power plant defect detection based on aerial images , and can realize real-time positioning of defective components, with a small amount of calculation and good real-time performance, thereby improving the efficiency of defect detection and positioning of intelligent photovoltaic power plants

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