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The Influence of the EVA Film Aging on the Degradation Behavior of PV Modules Under High Voltage Bias in Wet Conditions Followed by Electroluminescence
摘要： The influence of the ethylene-vinyl acetate (EVA) film quality on potential induced degradation was studied on in-house developed mini modules with p-type monocrystalline silicon solar cells. The modules were assembled with EVA films of equivalent qualities, but different ages and exposed to an accelerated test (relative humidity = 85%, T = 60 °C, Vbias = +1000 V). The age of the EVA film was determined from the time we received the EVA film, and opened the sealed enclosure and the time of lamination. After the EVA film was removed from the sealed enclosure, it was kept in a dark place at room temperature. The storage times of the “fresh,” “aged,” and “expired” films were: less than 14 d, around 5 mo, and more than 5 years, respectively. While modules with a “fresh” EVA film exhibit almost no degradation, the modules with the “aged” EVA film degrade very rapidly and severely. Their degradation rate was around 0.2%/d during the 2000 h of damp heat test. We also observed a strong silver line corrosion, which occurs because of the peroxide leftovers in the “aged” EVA films.
关键词： photovoltaic (PV) modules,high voltage stress,EVA film,Degradation,potential induced degradation (PID),leakage current
更新于2025-09-23 15:21:01
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Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning
摘要： With the rising use of photovoltaic and ongoing installation of large-scale photovoltaic systems worldwide, the automation of photovoltaic monitoring methods becomes important, as manual/visual inspection has limited applications. This research work deals with automatic detection of photovoltaic module defects in Infrared images with isolated deep learning and develop-model transfer deep learning techniques. An Infrared images dataset containing infrared images of normal operating and defective modules is collected and used to train the networks. The dataset is obtained from Infrared imaging performed on normal operating and defective photovoltaic modules with lab induced defects. An isolated learned model is trained from scratch using a light convolutional neural network design that achieved an average accuracy of 98.67%. For transfer learning, a base model is first developed (pre-trained) from electroluminescence images dataset of photovoltaic cells and then fine-tuned on infrared images dataset, that achieved an average accuracy of 99.23%. Both frameworks require low computation power and less time; and can be implemented with ordinary hardware. They also maintained real time prediction speed. The comparison shows that the develop-model transfer learning technique can help to improve the performance. In addition, we reviewed different kind of defects detectable from infrared imaging of photovoltaic modules, that can help in manual labelling for identifying different defect categories upon access to new huge data in future studies. Last of all, the presented frameworks are applied for experimental testing and qualitative evaluation.
关键词： Isolated deep learning,Develop-model transfer deep learning,Automatic defect detection,Thermography,Infrared images,Photovoltaic (PV) modules
更新于2025-09-19 17:13:59
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Photovoltaic Module Reliability || Qualification Testing
摘要： Chapter 3 described the types of accelerated stress tests that are typically applied to photovoltaic (PV) modules. These tests are used in research to evaluate how well modules perform in relation to the specific stresses applied during these tests. However, what the industry needs is a defined set of accelerated stress tests that can be applied to all modules in the same way. Such a set of tests are called Qualification Tests or often in Europe are referred to as Type Approval Testing. Qualification tests are a set of well‐defined accelerated stress tests developed out of a reliability program. They incorporate strict pass/fail criteria. Hoffman and Ross [1] defined the purpose of qualification testing as a means of rapidly detecting the presence of known failure or degradation modes in the intended environment(s). The stress levels and durations are limited so the tests can be completed within a reasonable amount of time and cost. One of the goals of Qualification testing is for a significant number of commercial module types to pass and that all subsequent production modules will be built the same way as the modules that were tested. Passing the Qualification test says the product meets the specific set of criteria, but doesn’t predict product lifetime nor indicate which product will last longer or which will degrade in operation. However, if properly designed, the Qualification test will be a good indicator that modules passing the test sequence will not suffer from infant mortality – that is they will survive for a reasonable amount of time in the field. The real usefulness of such a Qualification test sequence can only be validated by assessing the field performance of products that have successfully passed the test sequence.
关键词： failure or degradation modes,reliability program,pass/fail criteria,photovoltaic (PV) modules,Type Approval Testing,field performance,Qualification Tests,infant mortality
更新于2025-09-19 17:13:59
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CNN based automatic detection of photovoltaic cell defects in electroluminescence images
摘要： Automatic defect detection is gaining huge importance in photovoltaic (PV) field due to limited application of manual/visual inspection and rising production quantities of PV modules. This study is conducted for automatic detection of PV module defects in electroluminescence (EL) images. We presented a novel approach using light convolutional neural network architecture for recognizing defects in EL images which achieves state of the art results of 93.02 % on solar cell dataset of EL images. It requires less computational power and time. It can work on an ordinary CPU computer while maintaining real time speed. It takes only 8.07 milliseconds for predicting one image. For proposing light architecture, we perform extensive experimentation on series of architectures. Moreover, we evaluate data augmentation operations to deal with data scarcity. Overfitting appears a significant problem; thus, we adopt appropriate strategies to generalize model. The impact of each strategy is presented. In addition, cracking patterns and defects that can appear in EL images are reviewed; which will help to label new images appropriately for predicting specific defect types upon availability of large data. The proposed framework is experimentally applied in lab and can help for automatic defect detection in field and industry.
关键词： PV cell cracking,Automatic defect detection,Convolutional neural network (CNN),Electroluminescence,Deep learning,Photovoltaic (PV) modules
更新于2025-09-19 17:13:59
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Simulation-Based Exergy Analysis of Large Circular Economy Systems: Zinc Production Coupled to CdTe Photovoltaic Module Life Cycle
摘要： The second law of thermodynamics (2LT) helps to quantify the limits as well as the resource efficiency of the circular economy (CE) in the transformation of resources, which include materials, energy, or water, into products and residues, some of which will be irreversibly lost. Furthermore, material and energy losses will also occur, as well as the residues and emissions that are generated have an environmental impact. Identifying the limits of circularity of large-scale CE systems, i.e., flowsheets, is necessary to understand the viability of the CE. With this deeper understanding, the full social, environmental, and economic sustainability can be explored. Exergy dissipation, a measure of resource consumption, material recoveries, and environmental impact indicators together provide a quantitative basis for designing a resource-efficient CE system. Unique and very large simulation models, linking up to 223 detailed modeled unit operations, over 860 flows and 30 elements, and all associated compounds, apply this thermoeconomic (exergy-based) methodology showing (i) the resource efficiency limits, in terms of material losses and exergy dissipation of the CdTe photovoltaic (PV) module CE system (i.e., from ore to metal production, PV module production, and end-of-life recycling of the original metal into the system again) and (ii) the analysis of the zinc processing subsystem of the CdTe PV system, for which the material recovery, resource consumption, and environmental impacts of different processing routes were evaluated, and the most resource-efficient alternative to minimize the residue production during zinc production was selected. This study also quantifies the key role that metallurgy plays in enabling sustainability. Therefore, it highlights the criticality of the metallurgical infrastructure to the CE, above and beyond simply focusing on the criticality of the elements.
关键词： Thermoeconomics,Geometallurgy,Process simulation,Jarosite,Circular economy,Sustainability,Digital twin,Exergy,CdTe photovoltaic (PV) modules
更新于2025-09-16 10:30:52