Deterioration Diagnosis of Solar Module Using Thermal and Visible Image Processing

Jeong, Heon; Kwon, Goo‐Rak; Lee, Sang‐Woong

doi:10.3390/en13112856

Cited by 22 publications

(21 citation statements)

References 16 publications

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“…However, the main limitation in the proposed approach was the insufficient number of available datasets, which affected the model's reliability. A previous study [33] presented a scheme for the image processing technique of thermal images for the automatic identification of solar modules that occur in a hot spot, and the experimental results exhibited 97% accuracy in the visual evaluation. Having a finite quantity of datasets does not make a model accurate.…”

Section: Infrared Thermographymentioning

confidence: 96%

Importance of Image Enhancement and CDF for Fault Assessment of Photovoltaic Module Using IR Thermal Image

et al. 2021

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Infrared thermography is the science of measuring the infrared energy emitted by an object, translating it to apparent temperature variance, and displaying the result as an infrared image. Significantly, acquiring thermal images delivers distinctive levels of temperature differences in solar panels that correspond to their health status, which is beneficial for the early detection of defects. The proposed algorithm aims to analyze the thermal solar panel images. The acquired thermal solar panel images were segmented into solar cell sizes to provide more detailed information by region or cell area instead of the entire solar panel. This paper uses both the image histogram information and its corresponding cumulative distribution function (CDF), useful for image analysis. The acquired thermal solar panel images are enhanced using grayscale, histogram equalization, and adaptive histogram equalization to represent a domain that is easier to analyze. The experimental results reveal that the extraction results of thermal images provide better histogram and CDF features. Furthermore, the proposed scheme includes the convolutional neural network (CNN) for classifying the enhanced images, which shows that a 97% accuracy of classification was achieved. The proposed scheme could promote different thermal image applications—for example, non-physical visual recognition and fault detection analysis.

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Section: Infrared Thermographymentioning

confidence: 96%

Importance of Image Enhancement and CDF for Fault Assessment of Photovoltaic Module Using IR Thermal Image

et al. 2021

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“…Alsafasfeh, M., et al [2] yes yes Hwang, M.-H., et al [4] yes bypass diode Libra, M., et al [8] yes yes Niccolai, A., et al [9] yes digital map Vieira, R.G., et al [10] yes bypass diode Navid, Q., et al [11] yes Henry, C., et al [12] yes yes Pierdicca, R., et al [13] yes yes deep learning Jeong, H., et al [14] yes yes diagnosis Boulhidja, S., et al [15] yes Tsanakas, J.A., et al [16] yes yes Tsanakas, J.A., et al [17] yes Gallardo-Saavedra, S., et al [18] yes Ballestín-Fuertes, J., et al [19] EL 1 Herraiz, Á.H., et al [20] yes yes Fernández, A., et al [21] yes yes 1 Electroluminescence Technique.…”

Section: Other Defect Detectionmentioning

confidence: 99%

Using UAV to Detect Solar Module Fault Conditions of a Solar Power Farm with IR and Visual Image Analysis

Liao

2021

Applied Sciences

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In recent years, solar energy has been regarded as one of the most important sustainable energy sources. Under the rapid and large-scale construction of solar farms, the maintenance and inspection of the health conditions of solar modules in a large solar farm become an important issue. This article proposes a method for detecting solar cell faults with unmanned aerial vehicle (UAV) equipped with a thermal imager and a visible light camera, and providing a fast and reliable detection method. The detection process includes a new concept of real-time monitoring of the detected area and analysis of the health of solar panels. An image process is proposed that may quickly and accurately detect the abnormality of a solar module. The whole process includes grayscale conversion, filtering, 3-D temperature representation, probability density function, and cumulative density function analysis. Ten cases in real fields have been studied with this process, including large scale solar farms and small size solar modules installed on buildings. Results show that the cumulative density function is a convenient way to determine the health status of the solar panel and may provide maintenance personnel a basis for determining whether replacement of solar cells is necessary for improving the overall power generation efficiency and simplify the maintenance process. It is worth noting that image recognition can increase the clarity of IR images and the cumulative chart can judge the defect rate of the cell. These two methods were combined to provide an instant, fast and accurate defect judgment.

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“…The Hough transform detects a line represented by Equation (2). = cos + sin (2) where represents the distance from the origin to closest point on the straight line, and represents the angle between the axis and the line connecting the origin with that closest point.…”

Section: Implementation Of Bldc Control Algorithmmentioning

confidence: 99%

“…As the photovoltaic system is becoming popular as a future-oriented green energy, there is a strong tendency to install more PV systems widely. However, as operation time is increasing, the efficiency of the PV system is decreasing due to several reasons such as contamination of the PV module's surface, back surface temperature, and shadows [1,2]. Thus, it is necessary to inspect the current status of the module surface regularly.…”

Section: Introductionmentioning

confidence: 99%

Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image

2020

Self Cite

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Recently, there have been many types of research applying drones with a thermal camera to detect deteriorations in photovoltaic (PV) modules. A thermal camera can measure temperatures on the surface of PV modules and find the deteriorated area. However, a thermal camera generally has a lower resolution than a visible camera because of the limitations of cost. Due to different resolutions between the visible and thermal cameras, there are often invalid frames from a thermal camera. In this paper, we describe a gimbal controller with a real-time image processing algorithm to control the angle of the camera to position the region of interest (ROI) in the center of target PV modules to solve this problem. We derived the horizontal angle and vertical position of ROI in visible images using image processing algorithms such as the Hough transform. These values are converted into a PID control signal for controlling the gimbal. This process makes the thermal camera capture the effective area of target PV modules. Finally, experimental results showed that the photovoltaic module’s control area was properly located at the center of the thermal image.

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Deterioration Diagnosis of Solar Module Using Thermal and Visible Image Processing

Cited by 22 publications

References 16 publications

Importance of Image Enhancement and CDF for Fault Assessment of Photovoltaic Module Using IR Thermal Image

Importance of Image Enhancement and CDF for Fault Assessment of Photovoltaic Module Using IR Thermal Image

Using UAV to Detect Solar Module Fault Conditions of a Solar Power Farm with IR and Visual Image Analysis

Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image

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