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

Pivem, Tatiani; Araujo, Felipe de Oliveira de; Araujo, Laura de Oliveira de; Oliveira, Gustavo Spontoni de

doi:10.5121/sipij.2019.10305

Cited by 9 publications

(4 citation statements)

References 22 publications

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“…Another solution to the problem is the analysis of the visual images that are normally taken together with the IRT images in the UAV, which enables the operator to discard hot spots caused by soiling. Automation of the task of detecting soiling in individual modules was proposed by Yang et al [113], Pivem et al [114] and Qasem et al [115] using DIP techniques. Similar techniques were employed by Wen et al [116], and by Karaköse and Firildak [117] to detect shadows over PV systems.…”

Section: Soilingmentioning

confidence: 99%

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

2022

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In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method.

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Section: Soilingmentioning

confidence: 99%

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

2022

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“…In regions where the solar panel set up is extensive, the nozzles are directly fastened to the array of the solar panels, wherein; the former is run by a microprocessor [2]. The system also has a Programmable Logic Controller and a web-based software interface, which can be scheduled and controlled by the panel operator when the need arises.…”

Section: Literature Surveymentioning

confidence: 99%

Illustration of Automatic Panel Cleaning System

Prasad¹,

Rithika²,

Radhika³

2020

IJERT

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With a drive to meet the growing energy demands and to curb the increasing global greenhouse emissions, clean energy is being ubiquitously harnessed. Among these, the solar technology seems to be the pinnacle in the offing. Several research groups strive hard to attain higher efficiency in solar technology. However, one of the major causes for the drastic reduction in efficiency of the solar panels is due to the effect of soiling. Soiling takes into consideration several factors like the settling of sand particles on the solar cells, accumulation of dust coverings and bird droppings. The efficiency of Photovoltaic (PV) cells is not only limited by soiling, but also by the bird droppings on the panels and layering of the snow, which is a significant cause that has a major impact on the efficiency. Thus, to maintain efficiency throughout, the PV panels have to be cleaned and monitored regularly. Many times, the cleaning agent or the water settles behind as a layer on the panel, which again yields to soiling. Thus, there is a need to restrain the dust particles (soiling) from being settled as a layer on the panels. This paper deals with the techniques using Artificial Intelligence and Computer Vision to prevent the soiling, thereby increasing the efficiency of the solar technologies. A drone affixed with an overhead camera to monitor the panels and an end effector to carry out the cleansing process is employed. The image captured is then processed by the Machine Vision techniques to assess the type of the soiling, thereby urging the drone for removing the soiled particles. Moreover, with the addition of Artificial Intelligence and Computer Vision, we are also able to draw useful insights on the impact caused by different factors that influence soiling and these predictions would be beneficial for us in taking preventive and corrective measures, which would be an advantage in the long run.

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“…This classification is binary similar to the previous study. Pivem et al 25 . analyzed images in the visible spectrum by using pixel frequency histograms in the red, green, and blue (RGB) channels.…”

Section: Introductionmentioning

confidence: 99%

“…This classification is binary similar to the previous study. Pivem et al 25 analyzed images in the visible spectrum by using pixel frequency histograms in the red, green, and blue (RGB) channels. The level of soiling in the solar modules was located and determined, distinguishing between the punctual and distributed soiling on the surface of the PV module.…”

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confidence: 99%

Estimation of the impact of natural soiling on solar module operation through image analysis

Cavieres

Salas

Barraza

et al. 2023

Progress in Photovoltaics

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This study presents a novel algorithm to evaluate the power losses in photovoltaic (PV) modules that are exposed to naturally accumulated soiling. The proposed algorithm uses red, green, and blue (RGB) images of the PV modules captured in the visible spectrum and measures features such as the average color intensity and standard deviation from every color channel. These features are then ranked based on the module performance by using statistical tools, and the most relevant set of features is selected. The selected features are used as an input, and the module power generation is used as the label to train an artificial neural network that outputs a continuous value that represents the instantaneous performance of the evaluated module when compared with a clean module operating under the same environmental conditions. The proposed method is effective for images captured within an irradiance ranging from 700 to 1000 (W/m2), with the predictions achieving an R2 score of 0.96 and RMSE of 0.74% for power losses ranging from 0% to 20% in natural soiling. This percentage corresponds to a clean condition, where irradiance is not used as an input, making it a cheap and reliable solution to monitor soiling conditions. This is the first work of its kind to demonstrate a correlation between the extracted color features of RGB images and module performance under outdoor conditions for the studied dataset. The proposed algorithm presents less accurate results when it is tested in modules exposed to non‐homogenous soiling, suggesting that the proposed methodology might be effective only for naturally accumulated soiling.

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

Cited by 9 publications

References 22 publications

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

Illustration of Automatic Panel Cleaning System

Estimation of the impact of natural soiling on solar module operation through image analysis

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