Condition monitoring system for solar power plants with radiometric and thermographic sensors embedded in unmanned aerial vehicles

Abstract

9The photovoltaic solar energy industry is expanding, and there is therefore a need to 10 increase and improve its maintainability, operating costs, availability, reliability, safety, 11 life cycle, etc. The aim of this article is to design, develop and check a new condition 12 monitoring system to detect dust in solar photovoltaic panels. The condition monitoring 13 system uses a radiometric sensor connected to an Arduino platform. This novel approach 14 is based on emissivity analysis produced over a surface and characterized with a low 15 emissivity value when dust appears. A thermographic camera is also employed to validate 16 the results provided by the radiometric sensor. The system is designed to be embedded in 17 an unmanned aerial vehicle. Radiometric data is sent and analysed, Internet of Things is 18 employed, and thermograms are stored for further processing. Several scenarios with a 19 real solar panel are used in the experiments, in which the angles and distances of the 20 sensors and surface conditions are studied. An analysis of the radiometric sensor provides 21 accuracy results, and the presence of dust is identified in all scenarios.22

Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments

“…As a result, the brightness temperature of the surface falls. Hence, the temperature decreases when the dust and sand gets accumulated [37], and under the larger areas with dust, the temperature dips further [37]. A similar pattern is observed in the results shown in Figure 6.…”

“…The sand deposition also affects temperature. Hence, a change in temperature is also expected [37]. Figure 6b,d show the LST based on the Landsat 8 data.…”

“…Landsat 8 thermal bands (band 10 and 11) measure the energy emitted from the earth's surface and can be used to retrieve the LST. The presence of sand particles on PV panels can show the change in LST [37]. The LST depends on the land surface type because of variations in the emissivity from heterogeneous surfaces such as vegetation cover, built-up, and bare soil.…”

“…This implies that the panels were cleaner in 2018 because of the use of robots or by other means [45]. case study experiment done by Márquez and Ramírez [37], reveals that thermographic analysis is an effective method in the detection of accumulated dust particles carried by the wind on the solar panels. Based on emissivity analysis, the value of the emissivity of dust was revealed to be very low.…”

Application of A Computer Vision Method for Soiling Recognition in Photovoltaic Modules for Autonomous Cleaning Robots

“…Robotic platforms can be combined with embedded algorithms, using Internet of Things (IoT) it and artificial intelligence algorithms, like computer vision techniques or convolutional neural networks. That are a various mode of operation like visual inspection [5,6], infrared thermography [7,8,9] for hot spots detection and operation of the PV plants and Electroluminescence (EL), method capable of detected beyond soiling and achieve damage detection, but requires detailed analysis of each solar panel cell. In the work [10] provides a probabilistic methodology for identifying various types of damage for solar cells.…”