Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds

Abstract

A low-cost unmanned aerial platform (UAV) equipped with RGB (Red, Green, Blue) and thermographic sensors is used for the acquisition of all the data needed for the automatic detection and evaluation of thermal pathologies on photovoltaic (PV) surfaces and geometric defects in the mounting on photovoltaic power stations. RGB imagery is used for the generation of a georeferenced 3D point cloud through digital image preprocessing, photogrammetric and computer vision algorithms. The point cloud is complemented with temperature values measured by the thermographic sensor and with intensity values derived from the RGB data in order to obtain a multidimensional product (5D: 3D geometry plus temperature and intensity on the visible spectrum). A segmentation workflow based on the proper integration of several state-of-the-art geomatic and mathematic techniques is applied to the 5D product for the detection and sizing of thermal pathologies and geometric defects in the mounting in the PV panels. It consists of a three-step segmentation procedure, involving first the geometric information, then the radiometric (RGB) information, and last the thermal data. No configuration of parameters is required. Thus, the methodology presented contributes to the automation of the inspection of PV farms, through the maximization of the exploitation of the data acquired in the different spectra (visible and thermal infrared bands). Results of the proposed workflow were compared with a ground truth generated according to currently established protocols and complemented with a topographic survey. The proposed methodology was able to detect all pathologies established by the ground truth without adding any false positives. Discrepancies in the measurement of damaged surfaces regarding established ground truth, which can reach the 5% of total panel surface for the visual inspection by an expert operator, decrease with the proposed methodology under the 2%. The geometric evaluation of the facilities presents discrepancies regarding the ground truth lower than one degree for angular parameters (azimuth and tilt) and lower than 0.05 m 2 for the area of each solar panel.

LSTM Networks for Overcoming the Challenges Associated with Photovoltaic Module Maintenance in Smart Cities

“…Neural networks have been widely used in recent years, particularly those that are convolutional [13][14][15]. In terms of image recognition [16][17][18][19], the research has focused on two different aspects. One focuses on cloud recognition as a means of predicting the output power of the PV module and the other on the recognition of hot spots or breaks in the panels through images captured by a drone or similar.…”

Drone Integration with BIM: A Review

“…This aspect has been mathematically and geometrically researched by considering camera technology and drone battery life as well as its flight speed and distance from the target [7,8]. The most promising optimized drone path is either vertical or horizontal strips in a zig-zag pattern over the site area [9,10].…”

Thermal Infrared and Visual Inspection of Photovoltaic Installations by UAV Photogrammetry—Application Case: Morocco

“…Dense point clouds offer new possibilities in the detection and evaluation of thermal pathologies in PV modules. In [43], a Structure from Motion process was used to obtain a dense RGB point cloud of inspected panels. This last was geo-referenced using nine ground control points.…”