Automating the maintenance of photovoltaic power plants

Muhammad, Bilal; Prasad, Ramjee; Nisi, Marco; Mennella, Alberto; Gagliarde, Graziano; Cianca, Ernestina; Marenchino, Davide; Angrisano, Antonio; Bernardi, Mario Luca; Addabbo, Pia; Ullo, Silvia Liberata

doi:10.1109/gws.2017.8300492

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…Automatic defects' geo-localization is also one of the most important aspects in PV plants inspection, especially for large-scale ones. In [41], this issue was addressed by using a UAV equipped with a custom-designed payload that includes, in addition to a thermal and RGB camera, a low-cost RTK GNSS receiver. This allowed locating defects within a centimeter accuracy as well as reducing the time and the cost of the operation.…”

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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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

View full text Add to dashboard Cite

show abstract

“…Te detection is carried out after images are captured by a thermal camera or UAV and then are processed in computer software. Te reviews, listed in Table 1, cover several insights regarding several algorithms used such as digital image processing (DIP) [23][24][25][26][27][28][29][30], deep learning (DL) [31], and other machine learning (ML) techniques [26,32], which have comparable results to detect modules with or without faults and classify it into a category. Despite that, those approaches were able to detect the module's fault but do not address its correlation to the module itself.…”

Section: Introductionmentioning

confidence: 99%

Fast Partial Shading Detection on PV Modules for Precise Power Loss Ratio Estimation Using Digital Image Processing

Setiawan,

Fathurrahman,

Pamungkas

et al. 2024

Journal of Electrical and Computer Engineering

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Maintaining the maximum performance of solar panels poses the foremost challenge for solar photovoltaic power plants in this era. One of the common PV faults which decreases PV power output is a hot spot which is caused by a prolonged local partial shading from objects, such as dust piles or animal waste. To prevent it, an enormous effort for PV inspection is needed especially for large solar power plants. Hence, automatic partial shading detection is critical in preventing PV hot spots to assist maintenance activities which are associated with a drop in energy output. This research developed fast partial shading detection application on PV modules using digital image processing to detect the hot spot and PV modules areas and afterwards calculate the PV systems power loss ratio. The proposed method demonstrated a hot spot detection rate of 94.74% and a module detection rate of 100%. The power loss ratio calculation is compared and validated using IV curve measurement and has 91.26% similarity value which is a feasible application for the real-world system.

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“…In [4 ], a method composed of two parts: K ‐means colour quantization and density‐based spatial clustering of application with noise are introduced. The study in [5 ] illustrates a method for inspecting the photovoltaic (PV) power station automatically. However, the methods presented above mainly focus on the colour and edge information in the image, but this information is always covered by the complex background and it is difficult to extract, especially for the traditional method.…”

Section: Introductionmentioning

confidence: 99%

Automatic hotspots detection based on UAV infrared images for large‐scale PV plant

Nie

Luo

2020

Electron. lett.

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Owing to the significantly installed capacity of solar energy during the past decades, the operation and maintenance of the large photovoltaic power station is a big challenge, as the manual inspection is labour‐intensive and expensive. This Letter presents a solution for the intelligent inspection of the hotspot with the unmanned aerial vehicle in the large‐scale photovoltaic plant. First, a traditional image processing method is presented to eliminate the noise and crop the infrared image, which can make the defected feature more obvious. Then, the module is extracted by the line segments detection. Finally, considering the great advance in the realm of the computer vision, a deep‐learning‐based method for automatic hotspot detection is proposed for locating the hotspot with the data augmentation. The deep‐learning‐based model can extract the hotspot feature by the training process in the data set. In the end, the performance of the method proposed is extensively evaluated and the numerical results prove the accuracy and precision of the model.

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Automating the maintenance of photovoltaic power plants

Cited by 7 publications

References 5 publications

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

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

Fast Partial Shading Detection on PV Modules for Precise Power Loss Ratio Estimation Using Digital Image Processing

Automatic hotspots detection based on UAV infrared images for large‐scale PV plant

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