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Remote Sens. 2017, 9(6), 631; doi:10.3390/rs9060631

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

1
Department of Cartographic and Land Engineering, University of Salamanca, Hornos Caleros, 50, 05003 Ávila, Spain
2
Applied Geotechnologies Research Group, University of Vigo, Rúa Maxwell s/n, Campus Lagoas-Marcosende, 36310 Vigo, Spain
*
Author to whom correspondence should be addressed.
Received: 20 February 2017 / Revised: 18 May 2017 / Accepted: 13 June 2017 / Published: 20 June 2017
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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 m2 for the area of each solar panel. View Full-Text
Keywords: 3D reconstruction; UAV; photogrammetry; computer vision; infrared thermography; point cloud; photovoltaic; solar energy; photovoltaic panel 3D reconstruction; UAV; photogrammetry; computer vision; infrared thermography; point cloud; photovoltaic; solar energy; photovoltaic panel
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

López-Fernández, L.; Lagüela, S.; Fernández, J.; González-Aguilera, D. Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds. Remote Sens. 2017, 9, 631.

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