Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

Kim, D.; Youn, Junhee; Kim, C.

doi:10.5194/isprs-archives-xlii-2-w6-179-2017

Cited by 39 publications

(29 citation statements)

References 7 publications

(9 reference statements)

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“…A statistical analysis of acquired thermal images of the surfaces of PV modules has been proposed for fault diagnosis [17], [19]. Another approach to temperature monitoring for a large number of PV modules consists of developing ad hoc low-cost IR smart sensors, as proposed in [18].…”

Section: Discussionmentioning

confidence: 99%

Thermographic and electrical characterization of a photovoltaic panel under partial shading conditions: a case study

et al. 2019

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Shading is one of the most critical factors that produces a reduction in power in photovoltaic (PV) modules. The main causes of shading are related to cloud cover; local specificity; natural characteristics; building and other civil works; and the presence of the PV system itself. A reduction in overall radiation produces a consequent reduction in electric power. Another more problematic effect is associated with the partial shading of the PV modules. The shaded cell behaves as a load, dissipating energy and increasing its temperature. This effect can involve irreversible changes to the PV module, with a decrease in performance that can even cause the destruction of the shaded cell.The main aim of this work is the development of a testing procedure for the performance evaluation of commercial PV modules in the presence of partial shading on one cell. Tests were carried out using thermographic and electric measurements and by varying the shading levels according to IEC standards. Shading up to total darkening is achieved by means of a number of filters that reduce the direct solar irradiance.As a case study, a complete characterisation of a 180 Wp polycrystalline PV module was performed according to the proposed testing procedure, showing that high temperatures can be measured on the shaded PV module surface even if only 50 % of the surface of one cell of the PV module is darkened.

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Section: Discussionmentioning

confidence: 99%

Thermographic and electrical characterization of a photovoltaic panel under partial shading conditions: a case study

et al. 2019

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“…Other parameters like distance and angle also have an impact on IR imaging. To detect faulty cells, the local standard deviation and mean intensity of every panel must be characterized which poses a constraint to this technology, although it can detect up to 97% of PV faults [142].…”

Section: Thermal Imaging-based Analysismentioning

confidence: 99%

Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review

et al. 2021

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The worldwide electricity supply network has recently experienced a huge rate of solar photovoltaic penetration. Grid-connected photovoltaic (PV) systems range from smaller custom built-in arrays to larger utility power plants. When the size and share of PV systems in the energy mix increases, the operational complexity and reliability of grid stability also increase. The growing concern about PV plants compared to traditional power plants is the dispersed existence of PV plants with millions of generators (PV panels) spread over kilometers, which increases the possibility of faults occurring and associated risk. As a result, a robust fault diagnosis and mitigation framework remain a key component of PV plants. Various fault monitoring and diagnostic systems are currently being used, defined by calculation of electrical parameters, extracted electrical parameters, artificial intelligence, and thermography. This article explores existing PV fault diagnostic systems in a detailed way and addresses their possible merits and demerits.

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“…For an automatic defects' detection, especially hotspots, state-of-art literature propose different solutions. Canny edge operator was used to identify the location of hotspots on a PV panel from terrestrial thermal infrared image [24] and from thermal infrared images captured with a UAV [39]. However, the noises within and outside the panels affect the quality of the results.…”

Section: Uav For Pv Inspectionmentioning

confidence: 99%

“…However, the noises within and outside the panels affect the quality of the results. Based on the extracted panel area polygons, [39] propose a methodology for panel fault diagnostics using statistical values of thermal intensity of each extracted panel. Mean intensity and standard deviation of each array row were considered as statistical parameters in a local detection rule to diagnose faults.…”

Section: Uav For Pv Inspectionmentioning

confidence: 99%

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

Zefri

ElKettani

Sebari

et al. 2018

Drones

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Being sustainable, clean, and eco-friendly, photovoltaic technology is considered as one of the most hoped solutions face to worldwide energetic challenges. Morocco joins this context with the inauguration of numerous clean energy projects. However, one key factor in making photovoltaic installations a profitable investment are regular and effective inspections in order to detect occurred defects. Unmanned aerial vehicles (UAV) are increasingly used in various inspection fields. In this respect, this work focuses on the use of thermal and visual imagery taken by UAV in the inspection of photovoltaic installations. Visual and thermal images of photovoltaic modules, obtained by UAV, from different installations, and with different acquisition conditions and parameters, were exploited to generate orthomosaics for inspection purposes. The methodology was tested on a dataset we have acquired by a mission in Rabat (Morocco), and also on external datasets acquired in Switzerland. As final results, several visual defects were detected in visual RGB and thermal orthomosaics, such as cracks, soiling, and hotspots. In addition, a procedure of semi-automatic hotspots’ extraction was also developed and is presented within this work. On the other side, various tests were conducted on the influence of some acquisition and processing parameters (images’ overlap, the ground sampling distance, the flying height, the use of ground control points, the internal camera parameters’ optimization) on the detection of defects and the quality of visual and thermal generated orthomosaics. In the end, the potential of UAV thermal and visual imagery in the inspection of photovoltaic installations was discussed in function of various parameters. On the basis of the discussion feedback, UAV were concluded as advantageous tools within the thematic of this project, which proves the necessity of their implementation in this context.

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Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

Cited by 39 publications

References 7 publications

Thermographic and electrical characterization of a photovoltaic panel under partial shading conditions: a case study

Thermographic and electrical characterization of a photovoltaic panel under partial shading conditions: a case study

Fault Diagnostic Methodologies for Utility-Scale Photovoltaic Power Plants: A State of the Art Review

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

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