Optimal Productivity in Solar Power Plants Based on Machine Learning and Engineering Management

Herraiz, Álvaro Huerta; Marugán, Alberto Pliego; Márquez, Fausto Pedro Garcı́a

doi:10.1007/978-3-319-93351-1_77

Cited by 14 publications

(10 citation statements)

References 20 publications

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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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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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“…The design and development of novel neural network is not the main objective of this paper. The R-CNN developed by Huerta et al [28] to validate the approach presented in this paper. The fault detection by neural network provides three variables about the failure: bbox, score and label.…”

Section: R-cnn and Statistical Analysis: Real Case Studymentioning

confidence: 99%

“…Kaplani [27] analyses the degradation effect with I-V curve, IR thermography and an algorithm is developed for detecting discoloration in PV cells. Huerta et al [28] proposes a recurrent convolutional neural network for the identification of hot spots. Segovia et al [29] developed a real case study based of simulating dust in PV panel and analyse the thermal data.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Identification for Maintenance Management

Ramírez

Márquez

2020

Proceedings of the Fourteenth International Conference on Management Science and Engineering Management

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Photovoltaic solar energy is increasing the energy production due to the technological advances, together to the initial investment reduction. Solar farms are being installed with larger production capacity, improving the technical challenge for developing correct and efficient maintenance management. The photovoltaic maintenance management requires to increase the reliability and reduce the operating costs. The photovoltaic panels inspection with unmanned aerial vehicles is an efficient condition monitoring technique, analyzing large areas and obtaining accurate thermographic images. Due to the large amount of data, it is necessary the use of image processing algorithms for automatic identification of faults. Despite these advances, it is required the identification of the type and the importance of the fault. This information will be used by the plant operators for developing efficient maintenance management plans. The novelty developed in this work is a robust decision system for photovoltaic maintenance management, based on the combination of image processing for fault detection and statistic techniques. The first phase of the methodology is the extraction of interest areas or possible faults with neural networks trained for this purpose. The second phase develops the statistical analysis of the radiometric data of the area detected as possible fault with neural network. The radiometry data of these areas will be analyzed with statistic models with the aim of detecting patterns for detect identification and quantification. A real case study of a solar plant is presented, and the results obtained with this methodology provide the positioning and importance of each defect, probing the strength of the method.

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“…The temperature extracted is in the form of excel file that helps in finding pixel co-ordinates of the faults. From the excel file we can find maximum temperature and locate co-ordinates onto the image [4]. Since the images captured were not according to the rules of thermography, this approach cannot give us accurate results.…”

Section: A Temperature Extractionmentioning

confidence: 99%

Solar Panel Fault Detection

Gosavi¹

2019

IJRASET

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Thermal imaging offers several advantages in solar panel inspection. Faults can clearly be seen on a thermal image and Thermal infrared camera captures the raw thermal images of solar panels. These raw thermal images most likely to have many important entities which will help us to analyse solar panel image in detail. By capturing radiometric images of solar panel we will be able to explore these entities to inspect solar panels. Large areas of solar photovoltaic farms can be captured for scanning purpose using thermal imaging camera. In this work, we are focusing on potential problem areas on solar panel that can be detected and rectify before actual problem occurs. We have observed characteristics of solar panel and faults to detect various faults on solar panel leading to early fault detection and thus helping reduction in energy losses. This paper introduces most effective method for fault detection and location on solar panel.

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Optimal Productivity in Solar Power Plants Based on Machine Learning and Engineering Management

Cited by 14 publications

References 20 publications

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

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

Fault Detection and Identification for Maintenance Management

Solar Panel Fault Detection

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