1,892 research outputs found

    Degradation Monitoring of Photovoltaic Plants: Advanced GIS Applications

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    In order to evaluate a photovoltaic (PV) plant performance, payback time, profitability and environmental impact, an analysis must be made of plant maintenance needs, module and wiring degradation, mismatches and dust effects and PV cell defects and faults. Although a wide range of studies can be found that show the theory and laboratory testing of how these circumstances may affect PV production, very few studies in the field have covered or quantified real degradation effects and faults using a systematic procedure. The authors have therefore reviewed the conditions of PV plants operating in Southern Europe, examining the most frequently found faults and types of degradation, and they look at how novel technologies, such as geographic information system (GIS) applications, can help maintainers, owners, and promoters to supervise and locate damaged PV modules and monitor their evolution and impact on plant working conditions. GIS applications in this field allow the organization of a geo-referenced database of the system, locating and supervising the thirds of each PV cell in the power plant. With this information, investors and maintainers can exert increased control on the PV plant performance and conduct better preventive maintenance measures. The examples given demonstrate that these sorts of applications can be applied both to large PV plants and to domestic installations

    Solar panel detection within complex backgrounds using thermal images acquired by UAVs

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    The installation of solar plants everywhere in the world increases year by year. Automated diagnostic methods are needed to inspect the solar plants and to identify anomalies within these photovoltaic panels. The inspection is usually carried out by unmanned aerial vehicles (UAVs) using thermal imaging sensors. The first step in the whole process is to detect the solar panels in those images. However, standard image processing techniques fail in case of low-contrast images or images with complex backgrounds. Moreover, the shades of power lines or structures similar to solar panels impede the automated detection process. In this research, two self-developed methods are compared for the detection of panels in this context, one based on classical techniques and another one based on deep learning, both with a common post-processing step. The first method is based on edge detection and classification, in contrast to the second method is based on training a region based convolutional neural networks to identify a panel. The first method corrects for the low contrast of the thermal image using several preprocessing techniques. Subsequently, edge detection, segmentation and segment classification are applied. The latter is done using a support vector machine trained with an optimized texture descriptor vector. The second method is based on deep learning trained with images that have been subjected to three different pre-processing operations. The postprocessing use the detected panels to infer the location of panels that were not detected. This step selects contours from detected panels based on the panel area and the angle of rotation. Then new panels are determined by the extrapolation of these contours. The panels in 100 random images taken from eleven UAV flights over three solar plants are labeled and used to evaluate the detection methods. The metrics for the new method based on classical techniques reaches a precision of 0.997, a recall of 0.970 and a F1 score of 0.983. The metrics for the method of deep learning reaches a precision of 0.996, a recall of 0.981 and a F1 score of 0.989. The two panel detection methods are highly effective in the presence of complex backgrounds

    Mot monitorering av fotovoltaiske kraftverk med fotoluminescensavbildning

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    It is predicted that the photovoltaic energy conversion will be the largest installed power capacity by 2027. The least costly option for new electricity generation in many of the world’s countries will be the utility-scale solar photovoltaic electricity generation. Accurate monitoring of solar plants for localizing and detecting faults is expected to be one of the critical tasks facing the energy industry. Imaging of photovoltaic modules for the purpose of fault detection can be more efficient and accurate compared to measurements of electrical parameters. Different spectral regions provide different types of information about a faulty module. Detection of photoluminescence, that is, radiation emitted upon band-to-band recombination after charge carrier excitation with an illumination source, has shown a great potential in the laboratory setting. In the recent years, the first approaches in the outdoor setting have been conducted on silicon modules with the Sun as an excitation source. However, the reflected sunlight overlaps spectrally with the emitted photoluminescence. The imaging apparatus detects the total signal out of which only a few percent represent the emitted photoluminescence. Several approaches for elimination of more than 95% of the total signal have been suggested in the recent years. They are either based on controlling the emission of photoluminescence during imaging to achieve a variation in signal strength and, thus, a separation from the reflected solar irradiance, or on filtering of reflected solar irradiance with specially designed, narrow band-pass filters. The former requires interfering with the production to modulate the operating point of the modules between two operating conditions. This has been implemented by using additional equipment connected physically to a certain number of modules it is dimensioned for and by moving it during imaging. We have tried to develop an approach for photoluminescence imaging which would enable imaging of as many modules as possible with as little interference as possible for an easier implementation on a utility-scale photovoltaic power plant. This has been done by using the capabilities of a string inverter to change the operating point of a string. The first approach is based on remote control of the operating point between two conditions. The second approach is far less invasive and takes the advantage of the string inverter’s built-in functionality to conduct current-voltage curve sweeps. Both approaches enable variation of the operating point on more than one string. The approach with current-voltage curve sweeps implies that a string undergoes an entire range of operating conditions, which results in a continuously changing photoluminescence signal. From such a data set one can obtain more information about modules’ defects than what is possible from an image set obtained during controlled modulation between two conditions. Therefore, it is more timeconsuming to process an image set acquired during a current-voltage curve sweep. We propose an alternative algorithm which performs better in case of unsupervised image processing in real time. This way of imaging and data processing is also applicable in irradiance conditions below 100 Wm−2. The mentioned aspects of our photoluminescence imaging approach and the novel algorithm make this technique promising for large-scale inspections.IfĂžlge prognoser vil fotovoltaisk energikonvertering vare den stĂžrste installerte effektkapasiteten innen 2027. I mange land vil storskala solcelleanlegg vĂŠre den rimeligste lĂžsningen for ny energiproduksjon. Presis monitorering av fotovoltaiske kraftverk med mĂ„l om lokalisering og detektering av feil forventes Ă„ vĂŠre Ă©n av energiindustriens kritiske oppgaver. Avbildning av fotovoltaiske moduler for Ă„ detektere feil kan vare mer effektivt og gi mer nĂžyaktige resultater enn mĂ„linger av elektriske parametere. Detektering av fotoluminescens med kamera, dvs. strĂ„ling avgitt fra halvledermaterialet silisium i forbindelse med bĂ„nd-til-bĂ„nd-rekombinasjon etter eksitasjon av elektroner med en lyskilde, har vist stort potensiale. De fĂžrste forsĂžkene med sola som eksitasjonskilde har blitt gjennomfĂžrt pĂ„ silisium moduler i de siste Ă„rene. Det reflekterte sollyset i det samme bĂžlgelengdeomrĂ„det som det fotoluminescerende signalet blir ogsĂ„ detektert av kamerautstyret. Fotoluminescens utgjĂžr kun noen fĂ„ prosent av det totale signalet. Flere metoder for Ă„ skille fotoluminescens fra det reflekterte sollyset har blitt foreslĂ„tt. De baserer seg enten pĂ„ kontrollert emisjon av fotoluminescenssignalet i lĂžpet av avbildningen for Ă„ oppnĂ„ en variasjon i signalet som skal gjĂžre det mulig Ă„ separere det fra det reflekterte sollyset, eller pĂ„ detektering av kun fotoluminescens gjennom spesiallagde, smale bĂ„nd-pass filtre. FĂžrstnevnte krever inngrep i modulenes strĂžmproduksjon for Ă„ styre operasjonspunktet mellom to tilstander. Dette kan gjennomfĂžres ved at tilleggsutstyr kobles pĂ„ modulene og flyttes i lĂžpet av avbildningen. Vi har jobbet med Ă„ utvikle en tilnĂŠrming for fotoluminescensavbildning av sĂ„ mange moduler sĂ„ mulig med sĂ„ lite inngrep som mulig. FormĂ„let har vĂŠrt Ă„ utvikle en avbildningsmetode som vil kunne gjennomfĂžres pĂ„ storskala solcelleanlegg. Dette har blitt gjort ved Ă„ utnytte funksjonalitetene til en strenginverter. Den ene tilnĂŠrmingen har vĂŠrt kontaktlĂžs styring av operasjonspunktet gjennom strenginverteren og dermed uten tilleggsutstyr som mĂ„ flyttes i lĂžpet av avbildningen. En forbedring av denne metoden baserer seg pĂ„ utnyttelse av strenginverterens innebygde egenskap til Ă„ skanne strĂžm-spenningsskarakteristikken til en gitt streng og er derfor betraktelig mindre inngripende. Begge tilnĂŠrmingene muliggjĂžr en endring i operasjonspunktet pĂ„ mer enn ÂŽen streng av gangen. TilnĂŠrmingen med skanningen av strĂžm-spenningsskarakteristikken innebĂŠrer at strengen(e) gĂ„r gjennom en hel rekke av operasjonstilstander som resulterer i et fotoluminescenssignal i kontinuerlig endring. En slik bildeserie gir mer informasjon om modulenes defekter enn en bildeserie tatt i lĂžpet av den kontrollerte styringen av operasjonspunktet mellom to tilstander. Det er derfor mer tidkrevende Ă„ prosessere en bildeserie samlet med den fĂžrstnevnte metoden. I den forbindelse foreslĂ„r vi en alternativ algoritme som gir bedre resultater med ikke-styrt bildebehandling i sanntid. Metoden er ogsĂ„ anvendelig ved veldig lave irradiansnivĂ„er, under 100 Wm−2. Metoden for fotoluminescensavbildning mens skanningen av strĂžm-spenningskarakteristikken pĂ„gĂ„r i kombinasjon med den nye algoritmen for bildebehandling er lovende for videre utvikling med hensyn pĂ„ storskala avbildning.The Research Center for Sustainable Solar Cell Technolog

    Imputation of missing data in photovoltaic panel monitoring system

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    In scientific research, data acquisition and processing play a fundamental role. In photovoltaic systems, given their nature, this process presents deficiencies due to various factors such as the dispersion of the installed modules, climatic conditions or the amount of information that must be obtained, so the processes of data acquisition, storage and processing are very important. The present research developed a data acquisition, storage and processing system for photovoltaic systems, following the European standards IEC 60904 and IEC 61724 for data acquisition, Fog Computing for information storage and finally Machine Learning was used for processing. The results showed that the KNN-based model obtained a SCORE of 99.08%, MAE of 25.3 and MSE of 93.16. Concluding that the KNN-based model is the most robust model for data imputation in PV system monitoring

    PV System Design and Performance

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    Photovoltaic solar energy technology (PV) has been developing rapidly in the past decades, leading to a multi-billion-dollar global market. It is of paramount importance that PV systems function properly, which requires the generation of expected energy both for small-scale systems that consist of a few solar modules and for very large-scale systems containing millions of modules. This book increases the understanding of the issues relevant to PV system design and correlated performance; moreover, it contains research from scholars across the globe in the fields of data analysis and data mapping for the optimal performance of PV systems, faults analysis, various causes for energy loss, and design and integration issues. The chapters in this book demonstrate the importance of designing and properly monitoring photovoltaic systems in the field in order to ensure continued good performance

    Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4

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    Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

    A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation

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    Nowadays, the rapid development of photovoltaic(PV) power stations requires increasingly reliable maintenance and fault diagnosis of PV modules in the field. Due to the effectiveness, convolutional neural network (CNN) has been widely used in the existing automatic defect detection of PV cells. However, the parameters of these CNN-based models are very large, which require stringent hardware resources and it is difficult to be applied in actual industrial projects. To solve these problems, we propose a novel lightweight high-performance model for automatic defect detection of PV cells in electroluminescence(EL) images based on neural architecture search and knowledge distillation. To auto-design an effective lightweight model, we introduce neural architecture search to the field of PV cell defect classification for the first time. Since the defect can be any size, we design a proper search structure of network to better exploit the multi-scale characteristic. To improve the overall performance of the searched lightweight model, we further transfer the knowledge learned by the existing pre-trained large-scale model based on knowledge distillation. Different kinds of knowledge are exploited and transferred, including attention information, feature information, logit information and task-oriented information. Experiments have demonstrated that the proposed model achieves the state-of-the-art performance on the public PV cell dataset of EL images under online data augmentation with accuracy of 91.74% and the parameters of 1.85M. The proposed lightweight high-performance model can be easily deployed to the end devices of the actual industrial projects and retain the accuracy.Comment: 12 pages, 7 figure

    Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

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    This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader

    Manufacturing of Photovoltaic Devices, Power Electronics and Batteries for Local Direct Current Power Based Nanogrid

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    To meet the current and future demands of electrical power for household, industrial, commercial and transport sectors, the energy infrastructure has to undergo changes in terms of generation, distribution and consumption. Due to the shortcomings of nuclear and fossil fuel based power generation, the emergence of renewable energy has provided a very lucrative option. With the advent of low-cost photovoltaics (PV) panels and our ability to generate, store and use electrical energy locally without the need for long-range transmission, the world is about to witness transformational changes in electricity infrastructures. For local nano-grids, direct current (DC) -based system has several distinct advantages that are demonstrated through theoretical and experimental results. A PV- powered and local DC power based nano-grids can be more efficient, reliable, cyber secured, and can easily adopt internet of things (IoT) platforms. With DC generation, storage and consumption, significant amount of energy can be saved that are wasted in back and forth conversion between AC and DC. In case of geomagnetic disturbances, such nano-grids will be more resilient compared to centralized distribution network. Free-fuel, i.e. sunlight, based local DC nano-grid can be the sustainable and cost effective solution for underdeveloped, developing and developed economies. To take advantage of this, the manufacturing of PV, power electronics and batteries have to follow the best practices that aid process control, quality improvement and potential cost reduction. Without proper process control, the variation will result in yield loss, inferior performance and higher cost of production. On many instances, these issues were not considered, and some technology such as perovskite solar cell, received a lot of attention as a disruptive technology. Through detailed technical and economic assessments, it was shown that the variability and lack of rigorous process control will result in a lower efficiency when perovskite thin film solar cells are connected together to form a module. Due to stability and performance reasons, it was showed the perovskite solar cell is not ideal for 2-terminal or 4-terminal multi-junction/tandem configuration with silicon cells. Power electronics also play a vital role in PV systems. The challenges and design rules for silicon carbide (SiC) and gallium nitride (GaN) based power device manufacturing were analyzed. Based on it, advanced process control (APC) based single wafer processing (SWP) tools for manufacturing SiC and GaN power devices are proposed. For energy storage, batteries play an important role in PV installation. Li-ion technology will become the preferred storage due to its capabilities. Incorporation of advanced process control, rapid thermal processing, Industrial IoT, etc. can reduce variability, improve performance and reduce quality-check failures and bring down the cost of electrochemical batteries. The combined approaches in manufacturing of PV, power electronics and batteries will have a very positive impact in the growth of PV powered DC ñ€“based nano-grids

    Cascaded Fuzzy Logic based Arc Fault Detection in Photovoltaic Applications

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