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Detection and location of fouling on photovoltaic panels using a drone-mounted infrared thermography system
Abstract
Due to weathering and external forces, solar panels are subject to fouling and defects after a certain amount of time in service. These fouling and defects have direct adverse consequences such as low-power efficiency. Because solar power plants usually have large-scale photovoltaic (PV) panels, fast detection and location of fouling and defects across large PV areas are imperative. A drone-mounted infrared thermography system was designed and developed, and its ability to detect rapid fouling on large-scale PV panel systems was investigated. The infrared images were preprocessed using the K neighbor mean filter, and the single PV module on each image was recognized and extracted. Combining the local and global detection method, suspicious sites were located precisely. The results showed the flexible drone-mounted infrared thermography system to have a strong ability to detect the presence and determine the position of PV fouling. Drone-mounted infrared thermography also has good technical feasibility and practical value in the detection of PV fouling detection. © 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).
... Solar energy utilization, exemplified by photovoltaic power stations, has gained substantial traction and is actively expanding. However, the prolonged operation of photovoltaic array components in demanding conditions underscores the critical need for meticulous inspection within these power stations (Peng Z. et al., 2017). Notably, within photovoltaic power stations, one prevalent issue is the occurrence of hot spots, a typical fault in photovoltaic power generation systems. ...
... As a non-contact hotspot detection method, it has become increasingly popular due to its minimal impact on PV modules. Nonetheless, the swift and precise acquisition of infrared images and accurate localization pose emerging challenges (Peng Z. et al., 2017 andQ. Chen et al., 2023). ...
... Elidrissi, et al., 2022) .Peng Zhang and others have developed a drone-mounted infrared thermography system designed specifically for rapid fouling detection on large-scale PV panels. This system preprocesses infrared images using a K-nearest neighbor mean filter and applies a combined local and global detection method for precise location of suspicious sites, demonstrating a strong capability for detecting and pinpointing PV fouling (Peng Z. et al., 2017). Furthermore, Nie and colleagues have presented a traditional image processing method combined with deep-learning-based techniques for hotspot detection in infrared images. ...
Photovoltaic power stations utilizing solar energy, have grown in scale, resulting in an increase in operational maintenance requirements. Efficient inspection of components within these stations is crucial. However, the large area of photovoltaic power generation, coupled with a substantial number of photovoltaic panels and complex geographical environments, renders manual inspection methods highly inefficient and inadequate for modern photovoltaic power stations. To address this issue, this paper proposes a method and system for hot spot detection on photovoltaic panels using unmanned aerial vehicles (UAVs) equipped with multispectral cameras. The UAVs capture visible and infrared images of the photovoltaic power plant, which are then processed for photogrammetry to determine imaging position and attitude. The infrared images are stitched together using this information, forming a geographically referenced overall image. Hot spot detection is performed on the infrared images, enabling the identification of faulty photovoltaic panels and facilitating efficient inspection and maintenance. Experimental trials were conducted at a photovoltaic power station in Qingyuan, Guangdong Province China. The results demonstrate the effectiveness of the proposed method in accurately detecting panels with hot spot faults.
... Typically, a drone equipped with thermal camera is operated wirelessly by a technician and images are captured and saved during the flight, which are later processed for fault detection. Most previous drone-based approaches [9][10][11] require manual drone control, which is an exhausting procedure since it involves manual labor. In addition, previous works [12][13][14] are unable to provide the exact locations of the defective solar panels, which further delays the recovery process. ...
... Although these methods [15][16][17] can detect and localize the defective modules, they have been tested on a small scale dataset or in an artificial environment. On the contrary, some studies [9][10][11] have focused on the detection of fouling in large-sale PV panel systems. In [9], a computer vision approach was applied to identify and detect anomalies in PV modules in large-scale PV plants. ...
... The authors integrated geographic information with the results of template matching algorithm applied to thermal images, allowing panel identification by assigning identifier to each module during different flight sessions. In [10], a drone-mounted infrared thermography system was designed to rapidly detect fouling on PV panel systems. K neighbor mean filter was used for preprocessing the infrared images and single PV module in each image was recognized and extracted. ...
In the last few decades, photovoltaic (PV) power station installations have surged across the globe. The output efficiency of these stations deteriorates with the passage of time due to multiple factors such as hotspots, shaded cell or module, short-circuited bypass diodes, etc. Traditionally, technicians inspect each solar panel in a PV power station using infrared thermography to ensure consistent output efficiency. With the advancement of drone technology, researchers have proposed to use drones equipped with thermal cameras for PV power station monitoring. However, most of these drone-based approaches require technicians to manually control the drone which in itself is a cumbersome task in the case of large PV power stations. To tackle this issue, this study presents an autonomous drone-based solution. The drone is mounted with both RGB (Red, Green, Blue) and thermal cameras. The proposed system can automatically detect and estimate the exact location of faulty PV modules among hundreds or thousands of PV modules in the power station. In addition, we propose an automatic drone flight path planning algorithm which eliminates the requirement of manual drone control. The system also utilizes an image processing algorithm to process RGB and thermal images for fault detection. The system was evaluated on a 1-MW solar power plant located in Suncheon, South Korea. The experimental results demonstrate the effectiveness of our solution.
... Використання тепловізорів для розвідок вже не новина [1][2][3]. При застосуванні їх на безпілот-нику існує багато особливостей, наприклад, умови розташування на борту апарата, вібрації від мото-ру, засвічення, фонові завади та ін. Тому необхід-но визначити сфери, в яких будуть використовува-тися системи спостереження, визначити необхідні характеристики для тепловізорів, а потім знайти шляхи для підвищення ефективності та точності вимірювання тепловізором з урахуванням усіх особливостей. ...
... Аналіз досліджень і публікацій У методі, що запропоновано у роботі [1], ви-користовують швидке виявлення об'єкта, визначи-ти координати його розташування тепловізором, що розміщений на БПЛА. Водночас, застосовують інфрачервону (ІЧ) систему візуалізації, що вико-ристовують безпілотники, на борту яких буде за-кріплений тепловізор. ...
... Ця система використовує сучасне ядро ІЧ термографії, що перевершує інші типові системи. Одними з таких переваг є розділь-на здатність, що складає 640х512, а також модуль зберігання даних, розроблений спеціально для те-пловізійних систем, які в режимі реального часу зберігають дані про зображення і відповідні до них дані глобальної GPS системи позиціонування [1]. Цей метод може забезпечити отримання зобра-ження з високою роздільною здатністю при міні-мальних витратах. ...
Вступ. У даній статті розглянуто найпоширеніші сфери застосування тепловізорів, що розміщені на безпілотних літальних апаратах. Авторами розглянуто відмінності у конструкціях крил та спричинені цим переваги і недоліки безпілотних літальних апаратів. Мета. Як головну мету роботи автори визначають проблему поліпшення вихідних параметрів тепловізійних камер. Основна частина. Особлива увага приділена типу конструкції крил безпілотного літального апарата, значенню місця розташування камери на корпусі, проблемі усунення нестабільності зображення, що може бути спричинена вібрацією від двигуна. Наведено конструктивні і цифрові можливості для підвищення стабільності зображення. Окрім цього, автор акцентує увагу на шляхах підвищення ефективності вимірювань тепловізором внаслідок узгодження характеристик приймача випромінювання з параметрами об’єктива. Наголошується, що використання спеціального набору матеріалів дозволяє підвищити якість виготовлення неохолоджуваних мікроболометрів. Наведено порівняльну характеристику основних готових рішень тепловізійних модулів, що можуть використовуватися на безпілотному літальному апаратові, а також метод оцінки якості зображення за допомогою розрахунку мінімальної ефективної різниці температур.Висновки. Розглянуті аналоги модулів тепловізорів дають підстави вважати, що окрім узгодження параметрів мікроболометричної матриці та об’єктива велику роль грає програмне забезпечення, а саме алгоритми обробки потоку кадрів для стабілізації зображення. Авторами отримали фізико-математичну модель тепловізора, що розташований на безпілотному літальному апаратові, та шляхи контролю найбільш важливих вихідних параметрів тепловізорів: просторової та температурної роздільної здатності, мінімальної роздільної різниці температур.
... Para detectar as bordas nas imagens, como proposto por Zhang et al. (2017), adotou-se o conhecido detector de bordas de Canny (1986) por ser preciso na detecção. ...
... Como proposto por Zhang et al. (2017) decidiu-se localizar os vértices dos módulos identificando-se as coordenadas onde ocorrem interseções entre retas. Para isso, da Eq. (8), tem-se que: ...
As instalações de usinas fotovoltaicas têm crescido significativamente em todo o mundo nos últimos anos. Os avanços tecnológicos e a competitividade econômica da energia solar fotovoltaica no Brasil também podem ser destacados como fatores decisivos para sua inclusão na matriz energética nacional. A termografia infravermelhaganhou atenção significativa devido à sua facilidade de uso e aplicabilidade em sistemas fotovoltaicos de grande porte. O método de inspeção de módulos fotovoltaicos é principalmente manual. Mas, em usinas fotovoltaicas de grande porte a baixa eficiência temporal da inspeção manual dificulta sua realização. Nos últimos anos, as comunidades acadêmicas e industriais tornaram-se interessadas em métodos de termografia infravermelha baseados em drones, eficientes em termos de tempo. Nesses métodos, um drone equipado com uma câmera termográfica geralmente é operado sem fio por um técnico, e as imagens são captadas e salvas durante o voo e depois processadas para detecção de módulos defeituosos. Este artigo propõe uma metodologia para monitoramento da temperatura de módulos fotovoltaicos em uma usina solar a partir de imagens captadas por drone através de termografia infravermelha, com o objetivo de facilitar a manutenção, identificação de módulos defeituosos e evitar verificações manuais mais demoradas em sistemas de grande porte. Técnicas de processamento de imagens e de visão computacional são utilizadas para identificar os módulos. Para processar as imagens, foi utilizado o software Octave para desenvolver algoritmos e uma interface gráfica.
... Considering the fact that most solar road PV panels are required to have a durability of 25 years, and this time frame is very long, it is necessary to perform a simulation test in the lab by comparing two different solar road cells mounted and unmounted, and then compare the degradation of both based on the short circuit current , the open-circuit voltage , and the fill factor . To verify the solar road's reliability and inspect its performance, not only I-V and P-V curves are to be measured, but also several inspection methods must be considered, i.e., electroluminescence (EL) [55][56][57][58], photoluminescence (PL) [59][60][61][62][63] or drone/in-suite thermal images [64][65][66]. ...
... This is where drone-based thermal imaging is employed to simplify inspecting solar assets [64]. An intelligent drone equipped with a wireless thermal camera can take pictures during flight, storing them and allowing later inspection of the images, as shown in Figure 10c [65]. However, despite the advantages of this system, there is a drawback in that it is manual labor intensive, and the system needs to identify the exact location of the faults, so the whole inspection process is delayed [66]. ...
The objective of this review paper is to provide an overview of the current state-of-the-art in solar road deployment, including the availability of anti-reflection and anti-soiling coating materials for photovoltaic (PV) technology. Solar roads are built using embedded PV panels that convert sunlight into electricity, which can be stored for later use. Prototypes of solar roads have been tested on various continents, but the lack of suitable PV materials has limited their effectiveness compared to conventional PV systems. By analyzing the existing literature on solar roads and PV materials, including anti-reflection and anti-soiling coatings, we aim to identify gaps in knowledge and propose an action plan to improve the resiliency, durability, and reliability of PV panels in solar road applications. This will enable the deployment of solar roads as a clean, renewable energy source.
... Objects with a temperature above absolute zero degrees will radiate infrared rays due to their molecular motion [18,106]. The Infrared thermography method converts the power signal radiated by the object into an electrical signal through the infrared detector. ...
... This technology has so far been used to locate and detect the heating pipe. The target position can be obtained by analyzing the thermal image of the infrared radiometer whereas it is easy to be disturbed by nearby heat sources [106]. At present, the main improvement and conducted researches are as follows: ...
Buried pipelines are an essential component of the urban infrastructure of modern cities. Traditional buried pipes are mainly made of metal materials. With the development of material science and technology in recent years, non-metallic pipes, such as plastic pipes, ceramic pipes, and concrete pipes, are increasingly taking the place of pipes made from metal in various pipeline networks such as water supply, drainage, heat, industry, oil, and gas. The location technologies for the location of the buried metal pipeline have become mature, but detection and location technologies for the non-metallic pipelines are still developing. In this paper, current trends and future perspectives of detection and location of buried non-metallic pipelines are summarized. Initially, this paper reviews and analyzes electromagnetic induction technologies, electromagnetic wave technologies, and other physics-based technologies. It then focuses on acoustic detection and location technologies, and finally introduces emerging technologies. Then the technical characteristics of each detection and location method have been compared, with their strengths and weaknesses identified. The current trends and future perspectives of each buried non-metallic pipeline detection and location technology have also been defined. Finally, some suggestions for the future development of buried non-metallic pipeline detection and location technologies are provided.
... Inspection carries the further risk of exposure to hazards from circuits with electrical currents of several amps. Deployment of Unmanned Aerial Vehicles (UAV) is an alternative solution for the inspection of photovoltaic modules [2,[5][6][7]. Diagnosis of solar panel failures from aerial infrared thermography techniques using UAVs can be a complex procedure. One challenge is in the acquisition of thermal images: the selection of instruments such as UAVs and cameras is essential to ensure an adequate diagnosis in photovoltaic systems. ...
... The environmental variables, such as external temperature, wind speed, and irradiance are measured to verify that they are within operating ranges used in similar experiments and that the acquired images can be analyzed. The irradiance must be greater than 500 W/m 2 [10], and wind speed 3 m/s < F.S ≤ 5 m/s [5]. The procurement procedure is completed by estimating the acquisition height and the capture angle of the camera so that the emission and reflection remain constant. ...
In this work, two segmentation techniques for photovoltaic (PV) solar panels are explored: filtering by area and the second to the method of active contours level-set method (ACM LS). Tuning these techniques enables the contours of the solar panels to be obtained. Once these contours are established, morphological operations are used to refine the obtained edges and the Hough transform technique is used to find the main lines in the image. The vertices are then found from the possible intersections between the lines. Finally, the vertices are brought to the desired position at scale according to the reference of the PV panels projection transformations. The extraction of the region of interest is evaluated from the Dice similarity coefficient (DSC), Intersection over Union (IoU), and recall segmentation metrics.
... The main application of UAVs, though, is in visual inspection, where they excel due to their freedom of movement along the three spacial dimensions [35], the ability to hover in place and the variety of cameras they can host. When equipped with thermal cameras, they can be used for thermography, offering for example extremely quick surveys of the status of solar panel fields [36]. Regular RGB cameras are exploited for photogrammetry [37] and acquiring photographs that are subsequently processed through Machine Learning (ML) techniques [38], aiming at automatically detecting surface cracks, concrete spalling, rust, humidity and other surface-level issues. ...
... In cases where micro-inverters or optimizers are installed, only the dirty panels will experience production loss, whereas a conventional photovoltaic inverter can impact the entire installation's production, [4]. To address this, various traditional and advanced cleaning methods are available, as depicted in Figure 3. Regular cleaning can help maintain optimal conditions for solar panels, thus ensuring maximum efficiency and income generation [20], [21]. ...
The objective of this study is to develop an automatic cleaning system for Photovoltaic (PV) solar panels using machine learning algorithms. The experiment includes two phases. Phase one is to perform testing and reading of the sensor in 4 different classes which include no-dust, little dust, dusty, and very dusty during day and night time. The reading was taken using a visual inspection of the solar panel and the sensor reading using a multimeter. Phase two uses supervised learning to test and calibrate the sensor using the KNN algorithm. The classification was done using the data gathered from the sensor with one of the main classes identified. A total of 800 readings were taken. The results show the sensor reading taken during the night was more stable and accurate due to the sensor’s sensitivity to noise which includes: heat and light during the daytime. Secondly, using machine learning (KNN algorithm) we get a 95% (with K=5) correct classification for the four main classes which determines the level of cleaning needed for the solar panel.
... The thermal aerial photos taken by UAV equipped with autopilot have been positioned to be applied as a standardized instrument to substitute in situ visual inspections and I-V curve tests for the defective solar module because of its time and cost efficiency [9,10]. The autopilot is activated by executing pre-defined waypoints following a specific flight plan. ...
Monitoring the malfunction of the solar cells (for instance, 156 mm by 156 mm) caused by the soil debris requires a very low flight altitude when taking aerial photos, utilizing the autopilot function of unmanned aerial vehicle (UAV). The autopilot flight can only operate at a certain level of altitude that can guarantee collision avoidance for flight obstacles (for instance, power lines, trees, buildings) adjacent to the place where the solar panel is installed. For this reason, aerial photos taken by autopilot flight capture unnecessary objects (surrounding buildings and roads) around the solar panel at a tremendous level. Therefore, the autopilot-based thermal imaging causes severe data redundancy with very few matched key-points around the malfunctioned solar cells. This study aims to explore the tracking capability on soil debris defects in solar cell scale between UAV video versus photo-mosaic. This study experimentally validated that the video-based thermal imaging can track the thermal deficiency caused by the malfunction of the solar cell at the level of the photo-mosaic in terms of correlation of thermal signatures (0.98–0.99), detection on spatial patterns (81–100%), and distributional property (90–95%) with 2.5–3.4 times more matched key-points on solar cells. The results of this study could serve as a valuable reference for employing video stream in the process of investigating soil debris defects in solar cell scale.
... Autopilot-based thermal imaging using still images is now a standard procedure, replacing in situ visual inspection and I-V curve tests because of its shorter time and higher cost efficiency. (3,4) Autopilot flight is conducted along predefined waypoints following a specific flight plan for the target area. Typically, urban solar panels are scattered and account for only 1% of the total roof area in a city. ...
... The main application of UAVs, though, is in visual inspection, where they excel due to their freedom of movement along the three spacial dimensions [35], the ability to hover in place and the variety of cameras they can host. When equipped with thermal cameras, they can be used for thermography, offering for example extremely quick surveys of the status of solar panel fields [36]. Regular RGB cameras are exploited for photogrammetry [37] and acquiring photographs that are subsequently processed through Machine Learning (ML) techniques [38], aiming at automatically detecting surface cracks, concrete spalling, rust, humidity and other surface-level issues. ...
Unmanned Aerial Vehicles are employed for vision-based modal analysis of civil infrastructure, as they overcome range limitations of fixed cameras and measure the oscillations of a structure up close. Nevertheless, their potential is not fully exploited: they are often piloted manually and one at a time, though one drone is unable to capture high resolution displacement of a whole structure. An approach is explored here, employing multiple drones simultaneously to estimate natural frequencies and modal shapes of a structure, by synchronizing their measurement. The ability of the method to detect modal parameter variations is assessed, such that it can identify anomalies in the structure. Procedures are applied to a test structure, yielding maximum natural frequency estimation errors of 0.2% with respect to accelerometers. The results suggest the accuracy of the approach is high enough to warrant further development and support autonomous, multi-drone applications to the inspection of the built environment.
... Several methods have already been proposed to address this issue. While older works used human expertise [6] and digital signal processing [7][8][9][10][11] or parametric models of a PV module [12], the recent research trends tend to use a variety of machine learning tools such as traditional artificial neural networks [13][14][15] and support vector machines [16][17][18]. The introduction of deep neural networks has substantially changed the quality of automatic detection. ...
The emergence of large photovoltaic farms poses a new challenge for quick and economic diagnostics of such installations. This article presents this issue starting from a quantitative analysis of the impact of panel defects, faulty installation, and lack of farm maintenance on electricity production. We propose a low-cost and efficient method for photovoltaic (PV) plant quality surveillance that combines technologies such as an unmanned aerial vehicle (UAV), thermal imaging, and machine learning so that systematic inspection of a PV farm can be performed frequently. Most emphasis is placed on using deep neural networks to analyze thermographic images. We show how the use of the YOLO network makes it possible to develop a tool that performs the analysis of the image material already during the flyby.
... To date, the method of detecting faults in installed units required technicians to individually inspect each unit. However, with the rise of drone-based technology, there has been a considerable amount of research proposing the use of thermal-camera-equipped drones for the monitoring of PV installation sites [43][44][45]. While a considerable amount of research on this is based on a technician controlling the drone himself, a new research article has proposed and tested an automatic detection system using drones [46]. ...
The accurate prediction of the performance output of photovoltaic (PV) installations is becoming ever more prominent. Its success can provide a considerable economic benefit, which can be adopted in maintenance, installation, and when calculating levelized cost. However, modelling the long-term performance output of PV modules is quite complex, particularly because multiple factors are involved. This article investigates the available literature relevant to the modelling of PV module performance drop and failure. A particular focus is placed on cracks and hotspots, as these are deemed to be the most influential. Thus, the key aspects affecting the accuracy of performance simulations were identified and the perceived relevant gaps in the literature were outlined. One of the findings demonstrates that microcrack position, orientation, and the severity of a microcrack determines its impact on the PV cell’s performance. Therefore, this aspect needs to be categorized and considered accordingly, for achieving accurate predictions. Additionally, it has been identified that physical modelling of microcracks is currently a considerable challenge that can provide beneficial results if executed appropriately. As a result, suggestions have been made towards achieving this, through the use of methods and software such as XFEM and Griddler.
... Autopilot-based thermal imaging with still imageries has secured a position where it can be used as a standardized procedure to replace in situ visual inspections and I-V curve tests for the inspection of solar panels due to time and cost efficiency [10,11]. In this regard, it could be a benchmark for evaluating the thermal signatures obtained from video mosaic. ...
The unmanned aerial vehicle (UAV) autopilot flight to survey urban rooftop solar panels needs a certain flight altitude at a level that can avoid obstacles such as high-rise buildings, street trees, telegraph poles, etc. For this reason, the autopilot-based thermal imaging has severe data redundancy—namely, that non-solar panel area occupies more than 99% of ground target, causing a serious lack of the thermal markers on solar panels. This study aims to explore the correlations between the thermal signatures of urban rooftop solar panels obtained from a UAV video stream and autopilot-based photomosaic. The thermal signatures of video imaging are strongly correlated (0.89–0.99) to those of autopilot-based photomosaics. Furthermore, the differences in the thermal signatures of solar panels between the video and photomosaic are aligned in the range of noise equivalent differential temperature with a 95% confidence level. The results of this study could serve as a valuable reference for employing video stream-based thermal imaging to urban rooftop solar panels.
... These improved IR images could provide more details about the types of defects. In [24], an IR thermography system on a drone was developed to detect and locate malfunctioning PV modules. The K neighbors mean filter and Canny technique were used to preprocess these images. ...
In recent years, with the rise of environmental awareness worldwide, the number of solar power plants has significantly increased. However, the maintenance of solar power plants is not an easy job, especially the detection of malfunctioning photovoltaic (PV) cells in large-scale or remote power plants. Therefore, finding these cells and replacing them in time before severe events occur is increasingly important. In this paper, we propose a hybrid scheme with three embedded learning methods to enhance the detection of malfunctioning PV modules with validated efficiencies. For the first method, we combine the improved gamma correction function (preprocess) with a convolutional neural network (CNN). Infrared (IR) thermographic images of solar modules are used to train the abovementioned improved algorithm. For the second method, we train a CNN model using the IR temperatures of PV modules with the preprocessing of a threshold function. A compression procedure is then designed to cut the time-consuming preprocesses. The third method is to replace the CNN with the eXtreme Gradient Boosting (XGBoost) algorithm and the selected temperature statistics. The experimental results show that all three methods can be implemented with high detection accuracy and low time consumption, and furthermore, the hybrid scheme provides an even better accuracy.
... Once environmental conditions are verified to be adequate for thermographic inspection, the UAV was positioned horizontally 2.0 m far apart the lowest side of panels, and at 2.3e2.7 m height from the base of the panels, as indicated by Fig. 3. Aiming to do the camera IFOV (1.889 mrad) to cover 2 times the area of a panel cell, an ideal height up to 2.3 m was established, similar as done in [9], although they did not consider the IFOV camera. In consequence, the thermograms resolution (336 Â 256 pixels) is high enough to get information about cells condition, unlike other similar works [10,11]where only global damages of panels can be detected because reported heights are greater than 20 m. ...
This article presents a dataset for thermal characterization of photovoltaic systems to identify snail trails and hot spot failures. This dataset has 277 thermographic aerial images that were acquired by a Zenmuse XT IR camera (7-13
μ
m
wavelength) from a DJI Matrice 100 1drone (quadcopter). Additionally, our dataset includes the next environmental measurements: temperature, wind speed, and irradiance. The experimental set up consisted in a photovoltaic array of 4 serial monocrystalline Si panels (string) and an electronic equipment emulating a real load. The conditions for images acquisition were stablished in a flight protocol in which we defined altitude, attitude, and weather conditions.
... If the panel is healthy, the post processing is finished, otherwise, in case of detecting a defect in the module, it is established the degradation percentage of the photovoltaic module. In (Zhang et al., 2017) it is used an octocopter DJI Spreading Wings S100 with a Flir Tau 2 on board. Additionally, it includes an image storage module that allows saving image and positioning data at the same time. ...
This study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.
Drone thermography is constantly evolving, particularly through its integration into 3D reconstruction workflows. This article presents these recent uses, applied to the study of heritage buildings, and more particularly to diagnosis before renovation. Through a case study in Belgium, the castle of Mozet, it is shown how thermal point clouds can be obtained from multi-sensor drones. High-resolution reconstructions with integrated infrared data are generated for the exterior of the buildings, but also for the interior. In addition to highlighting potential thermal weaknesses of the envelope, the processing of such point clouds also allows new types of analysis. Moreover, the proposed workflow is highly automatable through scientific programming.
Tegenwoordig maakt het gebruik van drones voor thermografische beelden een grote evolutie door, vooral op het gebied van thermische 3D-reconstructies. In dit artikel worden de recente ontwikkelingen gepresenteerd, toegepast op de studie van erfgoedgebouwen, met name op de diagnose ervan vóór renovatie. Aan de hand van een casestudy in België, namelijk het kasteel van Mozet, wordt aangetoond hoe thermische puntenwolken kunnen worden verkregen met multi-sensor drones. In het artikel wordt er ingegaan op het proces om hoogwaardige reconstructies te maken met geïntegreerde infraroodgegevens voor zowel de buiten-als de binnenzijde van gebouwen. Het gebruik van zulke 3D thermische puntenwolken onthult niet alleen mogelijke zwaktes in de buitenschil van het gebouw, maar maakt ook nieuwe soorten analyses mogelijk. Bovendien kan de voorgestelde workflow grotendeels geautomatiseerd worden door middel van wetenschappelijke programmering.
The energy crisis and environmental problems have attracted global attention, thus the photovoltaic (PV) power generation technology comes to people’s mind. The application of unmanned aerial vehicle (UAV) inspection technology can overcome the disadvantages of large scale and high risk of this project. The application of unmanned aerial vehicle (UAV) infrared detection technology in PV power generation can not only improve work efficiency, but also have high economic benefits. This paper based on U-Net network and HSV space, proposes a method of PV infrared image segmentation and location detection of hot spots, which is used to detect and analyze the shielding of PV panels. Firstly, the main PV modules are automatically split from the different infrared image background based on U-Net. In order to quickly locate the defection location, the mask image is multiplied by the original image and then converted to HSV. The discriminant of bright spot features is introduced, and the discriminant mechanism is summarized according to the experiment, and the formation reason is analyzed. The experiment result shows that the method is not affected by the infrared image under the different background, provides data for the maintenance of power station and improves the detection accuracy. The accuracy rate of analyzing the causes of defects is 92.5%.
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.
It is common practice for unmanned aerial vehicle (UAV) flight planning to target an entire area surrounding a single rooftop’s photovoltaic panels while investigating solar-powered roofs that account for only 1% of the urban roof area. It is very hard for the pre-flight route setting of the autopilot for a specific area (not for a single rooftop) to capture still images with high overlapping rates of a single rooftop’s photovoltaic panels. This causes serious unnecessary data redundancy by including the surrounding area because the UAV is unable to focus on the photovoltaic panel installed on the single rooftop. The aim of this research was to examine the suitability of a UAV video stream for building 3-D ortho-mosaics focused on a single rooftop and containing the azimuth, aspect, and tilts of photovoltaic panels. The 3-D position accuracy of the video stream-based ortho-mosaic has been shown to be similar to that of the autopilot-based ortho-photo by satisfying the mapping accuracy of the American Society for Photogrammetry and Remote Sensing (ASPRS): 3-D coordinates (0.028 m) in 1:217 mapping scale. It is anticipated that this research output could be used as a valuable reference in employing video stream-based ortho-mosaics for widely scattered single rooftop solar panels in urban settings.
The efficient condition monitoring and accurate module defect detection in large-scale photovoltaic (PV) farms demand for novel inspection method and analysis tools. This paper presents a deep learning based solution for defect pattern recognition by the use of aerial images obtained from unmanned aerial vehicles (UAVs). The convolutional neural network (CNN) is used in the machine learning process to classify various forms of module defects. Such supervised learning process can extract a range of deep features of operating PV modules. It significantly improves the efficiency and accuracy of asset inspection and health assessment for large-scale PV farms in comparison with the conventional solutions. The proposed algorithmic solution is extensively evaluated from different aspects, and the numerical result clearly demonstrates its effectiveness for efficient defect detection of PV modules.
The paper is discussing about infrared scanning of PV solar plants. It is important that the performance of each solar panel and cell is verified. One new possibility compared to traditional ground-based scanning (handheld camera) is the utilization of UAV (Unmanned Aerial Vehicle). In this paper results from a PV solar Plant in Western Greece are introduced. The nominal power of the solar plants were 0, 9 MW and 2 MW and they were scanned both by a ground-controlled drone and by handheld equipment. It is essential to know all the factors effecting to results and also the time of scanning is important. The results done from the drone and from ground-based scanning are compared; also results from various altitudes and time of day are discussed.
The UAV (Unmanned Aerial Vehicle/RPAS (Remote Piloted Aircraft Systems) will give an excellent opportunity to monitor various targets which are impossible or difficult to access from the ground. Compared to fixed-wing and helicopter-based platforms it will give advantages but also this technology has limitations. One limitation is the weight of the equipment and the short operational range and short flight time. Also valid procedures must be created for different solutions in the future. The most important thing, as in all infrared thermography applications, is the proper interpretation of results.
This paper deals with the possibility of automated detection of defective solar cells within the whole solar module taken by electroluminescence methods. There is variety of possibilities to make a useful analysis of the quality of used material and technology processes during the manufacturing of solar cell and modules. One of the best from these methods that are capable to show the distribution of defects in the structure we can consider the electroluminescence which can detect defects in the fastest way. This method used in manufacturing process creates large array of data displaying local behavior of solar cell and panel structure. During the manufacturing of solar cell there is possible to make a lot of useful analysis of the quality of used material and technology processes. Among methods that are displaying distribution of defect in the structure we can count the photoluminescence, which is possible to use on uncontacted material, electroluminescence and LBIC (Light Beam Induced Current) eventually LBIV (Light Beam Induced Voltage) which can be used on contact made solar cells. All this methods are creating large array of data displaying local characteristic of solar cell structure. This data can be displayed as images or can by analyze by mathematic matrix software like Mathlab. The advantage of matrix analyze is possibility to automate the analyze process and speed up the whole process. For useful analyze there must be decide what type of defect can by automatic detected (to make the defect database), mask contact grid of analyzed data and by comparison image analyzing process found out if the solar cell contain a defect and than what type of defect is it. The only usable method for analyzing of defects in whole solar module is electroluminescence because the other method are not able to get information of all solar cells in whole module.
Silicon nanowire possesses great potential as the material for renewable energy harvesting and conversion. The significantly reduced spectral reflectivity of silicon nanowire to visible light makes it even more attractive in solar energy applications. However, the benefit of its use for solar thermal energy harvesting remains to be investigated and has so far not been clearly reported. The purpose of this study is to provide practical information and insight into the performance of silicon nanowires in solar thermal energy conversion systems. Spectral hemispherical reflectivity and transmissivity of the black silicon nanowire array on silicon wafer substrate were measured. It was observed that the reflectivity is lower in the visible range but higher in the infrared range compared to the plain silicon wafer. A drying experiment and a theoretical calculation were carried out to directly evaluate the effects of the trade-off between scattering properties at different wavelengths. It is clearly seen that silicon nanowires can improve the solar thermal energy harnessing. The results showed that a 17.8 % increase in the harvest and utilization of solar thermal energy could be achieved using a silicon nanowire array on silicon substrate as compared to that obtained with a plain silicon wafer.
This study provides an automatic method for detecting finger interruptions in electroluminescence (
EL
) images of multicrystalline solar cells. The proposed method is a supervised classification method. We obtain regions of interest (
ROI
) by separating the
EL
image to several regions. The fingers within each
ROI
are candidates for defect detection. We horizontally scan each
ROI
region and extract features from each finger pixel. In the training stage, we record a set of features which are extracted from interrupted fingers and noninterrupted fingers. These features are represented as points in a spectral embedding space produced by spectral clustering method. These points will be classified into two clusters: interrupted fingers and noninterrupted fingers. In the classification stage, we firstly detect the position of fingers in an
EL
image and obtain features from each finger. The set of features in each finger combined with known features in the training stage will be represented as points in the spectral embedding space and then will be classified to the cluster with nearer cluster centroid of known features. Experimental results show that the proposed method can effectively detect finger interruptions on a set of
EL
images of various solar cells.
We discuss the evolution and state-of-the-art of the use of Unmanned Aerial Systems (UAS) in the field of Photogrammetry and Remote Sensing (PaRS). UAS, Remotely-Piloted Aerial Systems, Unmanned Aerial Vehicles or simply, drones are a hot topic comprising a diverse array of aspects including technology, privacy rights, safety and regulations, and even war and peace. Modern photogrammetry and remote sensing identified the potential of UAS-sourced imagery more than thirty years ago. In the last five years, these two sister disciplines have developed technology and methods that challenge the current aeronautical regulatory framework and their own traditional acquisition and processing methods. Navety and ingenuity have combined off-the-shelf, low-cost equipment with sophisticated computer vision, robotics and geomatic engineering. The results are cm-level resolution and accuracy products that can be generated even with cameras costing a few-hundred euros. In this review article, following a brief historic background and regulatory status analysis, we review the recent unmanned aircraft, sensing, navigation, orientation and general data processing developments for UAS photogrammetry and remote sensing with emphasis on the nano-micro-mini UAS segment.
Microcracks at the device level in bulk solar cells are the current subject of substantial research by the photovoltaic (PV) industry. This review paper addresses nondestructive testing techniques that are used to detect microfacial and subfacial cracks. In this paper, we mainly focused on mono- and polycrystalline silicon PV devices and the root causes of the cracks in solar cells are described. We have categorized these cracks based on size and location in the wafer. The impact of the microcracks on electrical and mechanical performance of silicon solar cells is reviewed. For the first time, we have used the multi-attribute decision-making method to evaluate the different inspection tools that are available on the market. The decision-making tool is based on the analytical hierarchy process and our approach enables the ranking of the inspection tools for PV production stages, which have conflicting objectives and multi-attribute constraints.
This paper presents a review of the machine detection systems for micro-crack inspection of solar wafers and cells. To-date, there are various methods and procedures that have been developed at various laboratories around the world to inspect solar wafers and solar cells for manufacturing defects. This paper on micro-crack detection offers a comprehensive and an up-to-date review of different detection strategies, describing the main features of each technique together with its strengths as well as weaknesses. This paper will benefit researchers and practitioners, especially those who want to develop automatic inspection systems for inspecting solar wafers or solar cells for micro-cracks.
Precision agriculture (PA) is the application of geospatial techniques and sensors (e.g., geographic information systems, remote sensing, GPS) to identify variations in the field and to deal with them using alternative strategies. In particular, high-resolution satellite imagery is now more commonly used to study these variations for crop and soil conditions. However, the availability and the often prohibitive costs of such imagery would suggest an alternative product for this particular application in PA. Specifically, images taken by low altitude remote sensing platforms, or small unmanned aerial systems (UAS), are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. Not surprisingly, there have been several recent studies in the application of UAS imagery for PA. The results of these studies would indicate that, to provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required. Moreover, it is suggested that such endeavors should involve the farmer, particularly in the process of field design, image acquisition, image interpretation and analysis.
The reduction of wafer thickness requires an improved quality control of the wafer strength, which is significantly influenced by cracks. We introduce a machine learning framework to establish photoluminescence (PL) imaging as an optical inspection technique for the detection of cracks in multi-crystalline silicon wafers. The specially derived algorithm enables reliable crack detection in spite of similar background structures in the PL image from grain boundaries and dislocations. Within an experiment on thin wafers with artificially induced cracks we evaluate our approach by comparing the PL detection results to the findings of an infrared-transmission system and fracto-graphical reference data. Based on the optical detection result, we derive a description of the crack structure. Since wafer strength may change after etching and thermal processes, wafer strength is analyzed during cell production and correlated to the optical detection results.
Two critical limitations for using current satellite sensors in real-time crop management are the lack of imagery with optimum spatial and spectral resolutions and an unfavorable revisit time for most crop stress-detection applications. Alternatives based on manned airborne platforms are lacking due to their high operational costs. A fundamental requirement for providing useful remote sensing products in agriculture is the capacity to combine high spatial resolution and quick turnaround times. Remote sensing sensors placed on unmanned aerial vehicles (UAVs) could fill this gap, providing low-cost approaches to meet the critical requirements of spatial, spectral, and temporal resolutions. This paper demonstrates the ability to generate quantitative remote sensing products by means of a helicopter-based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors. During summer of 2007, the platform was flown over agricultural fields, obtaining thermal imagery in the 7.5-13-mum region (40-cm resolution) and narrowband multispectral imagery in the 400-800-nm spectral region (20-cm resolution). Surface reflectance and temperature imagery were obtained, after atmospheric corrections with MODTRAN. Biophysical parameters were estimated using vegetation indices, namely, normalized difference vegetation index, transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index, and photochemical reflectance index (PRI), coupled with SAILH and FLIGHT models. As a result, the image products of leaf area index, chlorophyll content ( C ab), and water stress detection from PRI index and canopy temperature were produced and successfully validated. This paper demonstrates that results obtained with a low-cost UAV system for agricultural applications yielded comparable estimations, if not better, than those obtained by traditional manned airborne sensors.
This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
The K-nearest neighbor (K-NN) filter introduced by Davis and
Rosenfeld (1978) has been long time successfully used in application to
noise smoothing problems. The main drawback of this filter is its very
high computational time. In this paper we introduce a slightly modified
version of the K-NN filter and show that these two filters have
practically the same performance in the noise removal sense while our
modification is simpler in implementation. A binary-tree search
technique for the implementation off the modified K-NN filter is
presented, and an efficient bit-serial architecture for implementation
of this filter is proposed
The electroluminescence (EL) scan is a powerful method to detect solar cells or modules with various defects. The electroluminescence measurement is used by default already in most cell and module production although an international standard determining pass and fail criteria is still missing. Furthermore, so far there was no suitable approach available for large-scale investigation of complete plants over 1 MW. One of the reasons is varying ambient conditions in real power plants that have a significant impact on the EL image quality during data acquisition in field. However, there is an increasing interest and demand in high volume EL-field inspections. Different systems are available, whereas most of them only operate during the night, because of less interfering radiation. In this paper two different methods for high volume EL image capture are compared including the usage of a drone generating EL videos of solar plants. The pros and cons and especially the influence of irradiation, wind and day time are investigated. Exemplarily results are presented obtained in a project where 12500 modules – potentially crack affected – have been investigated. The software-based data analysis revealed that crack-affected cells were mainly located in the center of the modules and PID affected modules were manly located at the edge.
aIR-PV-check is a method using an unmanned aerial vehicle UAV with an IR-camera for the quality inspection of PV-plants under real operating conditions. It enables the fast and 100%-control of PV-plants without handling the modules and initiating new defects. Defective modules are detected easily by their irregular temperature distribution. Besides the meteorological conditions, the interaction of defective modules within a string influences the module performance significantly. As measured and simulated data show the defect temperature as well as the output power of defective modules differ with the string configuration and string constellation. Thus, modules with extremely high temperatures are predominantly expected / detected in strings with only one single defective module. In a different constellation the same module may have a much lower defect temperature and a higher output power. Consequently, string configurations and string constellations of defective modules have a significant influence on the quality evaluation of modules, strings and generators.
Failure-free operation of solar panels is of fundamental importance for modern commercial solar power plants. To achieve higher power generation efficiency and longer panel life, a simple and reliable panel evaluation method is required. By using thermal infrared imaging, anomalies can be detected without having to incorporate expensive electrical detection circuitry. In this paper, we propose a solar panel defect detection system, which automates the inspection process and mitigates the need for manual panel inspection in a large solar farm. Infrared video sequences of each array of solar panels are first collected by an infrared camera mounted to a moving cart, which is driven from array to array in a solar farm. The image processing algorithm segments the solar panels from the background in real time, with only the height of the array (specified as the number of rows of panels in the array) being given as prior information to aid in the segmentation process. In order to “count” the number the panels within any given array, frame-to frame panel association is established using optical flow. Local anomalies in a single panel such as hotspots and cracks will be immediately detected and labeled as soon as the panel is recognized in the field of view. After the data from an entire array is collected, hot panels are detected using DBSCAN clustering. On real-world test data containing over 12,000 solar panels, over 98% of all panels are recognized and correctly counted, with 92% of all types of defects being identified by the system.
According to the distinct temperature difference characteristic between solar cells under different operation states, a new analysis and recognizing approach for the photovoltaic array operation states was proposed based on the infrared image analysis in the paper. At first, the infrared images of photovoltaic arrays are pre-processed and analyzed; the abnormal areas and their features are abstracted. To deal with influences of some factors on the temperature of photovoltaic, such as environment temperature, wind speed and solar illumination, a fuzzy reasoning technique based on the data fusion is employed and the fault areas are recognized automatically. The research results show that the normal, shadowed and aging destroyed operation states of photovoltaic array in power system can be recognized accurately with the proposed approach.
This paper presents a review and assessment of public-policy options for supporting large-scale penetration of photovoltaics (PV) in the United States. The goal therein is to reduce the costs both of solar technology and of grid integration, so enabling solar deployment nationwide. In this context, we analyze the solar PV markets and the solar industry globally, and discuss the external benefits of PV that must be advertised, and perhaps marketed, to assure an increase in social support for PV. We discuss existing energy-policy mixes in those countries leading to the development of solar power, highlighting the lessons learnt, and outlining areas of improvement of the existing policy mix in the United States. We highlight that there is a need for a holistic approach including social in addition to economic considerations, and we discuss policy options for supporting the continuation of PV market growth when the current investment tax credits expire.
Solar cells that convert sunlight into electrical energy are the main component of a solar power system. Quality inspection of solar cells ensures high energy conversion efficiency of the product. The surface of a multi-crystal solar wafer shows multiple crystal grains of random shapes and sizes. It creates an inhomogeneous texture in the surface, and makes the defect inspection task extremely difficult. This paper proposes an automatic defect detection scheme based on Haar-like feature extraction and a new clustering technique. Only defect-free images are used as training samples. In the training process, a binary-tree clustering method is proposed to partition defect-free samples that involve tens of groups. A uniformity measure based on principal component analysis is evaluated for each cluster. In each partition level, the current cluster with the worst uniformity of inter-sample distances is separated into two new clusters using the Fuzzy C-means. In the inspection process, the distance from a test data point to each individual cluster centroid is computed to measure the evidence of a defect. Experimental results have shown that the proposed method is effective and efficient to detect various defects in solar cells. It has shown a very good detection rate, and the computation time is only 0.1 s for a 550 × 550 image.
In the use of crystalline silicon solar cells, the micro defects, such
as cracks, the grain boundary dislocation, broken metal grid fingers,
etc., will seriously affect the efficiency and the life of crystalline
silicon solar cells. Therefore, it is necessary to detect the micro
defects of Si solar cells rapidly and accurately in the production
process. In this paper, firstly, the relationship between the
electroluminescence (EL) intensity from Si solar cells under the forward
bias and minority carrier diffusion length is simulated based on the
calculation under the condition of ideal P-N junction model. There
exists one to one quantitative agreement. We find that the relationship
referred above is nonlinear. Secondly, the relationship between the
defects in Si solar cells and minority carrier diffusion length (EL
intensity) is summed up. The defects and minority carrier lifetime are
also in accord with this relationship. Based upon these, the micro
defects in Si solar cells could be made out in theory. With experiments,
the defects in c-Si solar cells and poly-Si solar cells are detected
clearly from EI images. Theory analysis and experiments show that the
method is reasonable and efficient.
A hyperspectral imaging system is developed and is used to identify cracks and fracture defects in solar cells. The basic principles and key technologies of this system are presented, along with a characterization of its performance. The system can provided both single-band images and spectrums of solar cells by laser scanning and hyperspectral imaging. The spectral angle mapper algorithm is used to identify cracks on the surface of solar cells. Experiment results show that this is a non-contact, no-destructive method for detecting cracks in solar cells.
Thermal imaging is a technique to convert the invisible radiation pattern of an object into visible images for feature extraction
and analysis. Infrared thermal imaging was first developed for military purposes but later gained a wide application in various
fields such as aerospace, agriculture, civil engineering, medicine, and veterinary. Infrared thermal imaging technology can
be applied in all fields where temperature differences could be used to assist in evaluation, diagnosis, or analysis of a
process or product. Potential use of thermal imaging in agriculture and food industry includes predicting water stress in
crops, planning irrigation scheduling, disease and pathogen detection in plants, predicting fruit yield, evaluating the maturing
of fruits, bruise detection in fruits and vegetables, detection of foreign bodies in food material, and temperature distribution
during cooking. This paper reviews the application of thermal imaging in agriculture and food industry and elaborates on the
potential of thermal imaging in various agricultural practices. The major advantage of infrared thermal imaging is the non-invasive,
non-contact, and non-destructive nature of the technique to determine the temperature distribution of any object or process
of interest in a short period of time.
KeywordsInfrared radiation–Thermal imaging–Quality–Agriculture–Food
In this study, infrared thermography (IR) was used to map the surface temperature distribution of solar cells while in the reverse bias mode. It was observed that some cells exhibited an inhomogeneity of the surface temperature resulting in localized heating (hot-spot). Using the scanning electron microscopy (SEM), the structural images of hot-spot areas revealed that hot-spot heating causes irreversible destruction of the solar cell structure. Different techniques were later used to analyze the elemental composition of the different regions of the solar cells. It was revealed that a direct correlation exists between areas of high impurity contaminants and hot-spot heating. Areas with high concentration of transition metals resulted in hot-spot formation. The results of all the samples are presented in detail in this paper.
Inspecting PV-plants using aerial, drone-mounted infrared thermography system
- Buerhop-Lutz
line-segment extraction based on KNN filter and modified Hough transform
- Qingyuan
Research and method of defect detection of solar panels based on infrared imaging
- Wang