IR real-time analyses for PV system monitoring by digital image processing techniques

Aghaei, Mohammadreza; Gandelli, A.; Grimaccia, Francesco; Leva, Sonia; Zich, R.E.

doi:10.1109/ebccsp.2015.7300708

Cited by 51 publications

(23 citation statements)

References 13 publications

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“…With the same aim, in [33,34] it is proposed an algorithm based on standard thermal image processing and Canny's edge detector, with similar results and without facing the problem of the reflection, artificial light sources and external hot-spots. M. Aghaei et al shows the defects in PV-modules using a Laplacian model and a Gaussian filter [35]; however the false hot-spots analysis is not addressed. Additionally, photovoltaic modules can be detected using calculation of the co-occurrence matrices for fragments of images, as shown in [36].…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact Minimization

et al. 2018

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Abstract:The installed capacity of solar photovoltaics has increased over the past two decades worldwide, evolving from a few small scale applications to a daily power source. Such growth involves a great impact over operating processes and maintenance practices. The RGB (red, green and blue) and infra-red monitoring of photovoltaic modules is a non-invasive inspection method which provides information of possible failures, by relating thermal behaviour of the modules to the operational status of solar panels. An adequate thermal measurement module strongly depends on the proper camera angle selection relative to panel's surface, since reflections and external radiation sources are common causes of misleading results with the unnecessary maintenance work. In this work, we test a portable ground-based system capable of detecting and classifying hot-spots related to photovoltaic module failures. The system characterizes in 3D thermal information from the panels structure to detect and classify hot-spots. Unlike traditional systems, our proposal detects false hot-spots associated with people or device reflections, and from external radiation sources. Experimental results show that the proposed diagnostic approach can provide of an adequate thermal monitoring of photovoltaic modules and improve existing methods in 12% of effectiveness, with the corresponding financial impact.

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Section: Introductionmentioning

confidence: 99%

Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact Minimization

et al. 2018

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“…The digital image processing allows to analyze the captured thermal images against degradation effects, heated areas due to high temperature, quality of image. There techniques use in digital image processing for the analysis of thermal images after the data acquisition involve image filtering, image enhancement, image segmentation, feature extraction etc [21].…”

Section: Operation On Matlab Digital Image Processingmentioning

confidence: 99%

Thermal Image Analysis and Segmentation to Study Temperature Effects of Cement and Bird Deposition on Surface of Solar Panels

Chaudhary¹,

Chaturvedi²

2017

IJIGSP

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Abstract-To obtain solar energy from solar photovoltaic panels is not a new task now a days. Solar panels are designed to give their maximum possible output when exposed to the solar radiations. The operating conditions of solar panels affected by the atmospheric conditions like dust, dirt, solar INSOLATION, temperature etc. Here the effect of cement and bird deposits on the surface of solar panels is considered. Temperature rise due to cement and bird deposits develop hot-spots and affects the electrical power output of the solar panel. The cement and bird deposits can be seen by visually on solar panel surface but their effect can not be visualized by naked eye. The infrared THERMOGRAPHY helps to analyze these effects by using thermal imaging camera. This paper focus to study the temperature effects of cement and bird deposition on solar panel surface by capturing thermal images along with analyzing the thermal images using MATLAB digital image processing for better understanding. Image segmentation for cement and bird deposits is performed using watershed transform to achieve the desired region of interest from thermal images.

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“…This procedure is not only slow and expensive but also dependent on the subjectivity and expertise of the operator. Some companies use unmanned aerial vehicles equipped with thermographic video cameras in a similar way as proposed in this studies [2,31,32], but the processing is performed manually through the visualization of the data recorded by a human operator. Thus, capture time is reduced but processing time and human subjectivity are still elements that affect the quality of the final product, although some researchers have tried to solve this subjectivity by using image processing algorithms [32].…”

Section: Introductionmentioning

confidence: 99%

“…Some companies use unmanned aerial vehicles equipped with thermographic video cameras in a similar way as proposed in this studies [2,31,32], but the processing is performed manually through the visualization of the data recorded by a human operator. Thus, capture time is reduced but processing time and human subjectivity are still elements that affect the quality of the final product, although some researchers have tried to solve this subjectivity by using image processing algorithms [32]. An alternative to the use of thermographic cameras is the integration of RGB cameras in the UAV [33,34]; the main drawback of this option is that the type of defect detected depends on the flight conditions.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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 m 2 for the area of each solar panel.

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IR real-time analyses for PV system monitoring by digital image processing techniques

Cited by 51 publications

References 13 publications

Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact Minimization

Photovoltaic Modules Diagnosis Using Artificial Vision Techniques for Artifact Minimization

Thermal Image Analysis and Segmentation to Study Temperature Effects of Cement and Bird Deposition on Surface of Solar Panels

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

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