53 research outputs found
Segmentation of Photovoltaic Module Cells in Electroluminescence Images
High resolution electroluminescence (EL) images captured in the infrared
spectrum allow to visually and non-destructively inspect the quality of
photovoltaic (PV) modules. Currently, however, such a visual inspection
requires trained experts to discern different kinds of defects, which is
time-consuming and expensive. Automated segmentation of cells is therefore a
key step in automating the visual inspection workflow. In this work, we propose
a robust automated segmentation method for extraction of individual solar cells
from EL images of PV modules. This enables controlled studies on large amounts
of data to understanding the effects of module degradation over time-a process
not yet fully understood. The proposed method infers in several steps a
high-level solar module representation from low-level edge features. An
important step in the algorithm is to formulate the segmentation problem in
terms of lens calibration by exploiting the plumbline constraint. We evaluate
our method on a dataset of various solar modules types containing a total of
408 solar cells with various defects. Our method robustly solves this task with
a median weighted Jaccard index of 94.47% and an score of 97.54%, both
indicating a very high similarity between automatically segmented and ground
truth solar cell masks
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Enamelling of glass by CO₂ laser treatment
Decoration of glass using enamel is interesting for hollow glassware as well as for flat glass sheets. Laser radiation can be used advantageously to increase the temperature of the product locally. The reactions which take place during CO₂ laser heat treatment of a conventional PbO-containing frit are discussed intensely. Pyrometrical temperature measurements accompany the experiments to investigate the temperature development during CO₂ laser irradiation. At temperatures above Tg optimal results concerning low surface roughness, high brightness and intensive colouration are obrained without deformation of the shape of the product. Very promising results show that higher temperatures can be achieved by laser application and therefore PbO-free frits can be used
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Firimg PbO - free glass enamels using the CW - CO₂ laser
The firing of glass enamels, which are applied to decorate glass products, is usually done in a furnace. Α new and suitable technique for firing glass enamels is to make use of the high-power CW-CO₂ laser. Its be am (wavelength 10.6 μm) induces a very fast heating of the enamelled glass within a small surface layer. This makes it possible to fire glass enamels at temperatures above 1000°C, without any deformation of the Substrate glass body In this temperature ränge an amount of the harmful component PbO in t he glass enamel is n o longer necessary The PbO-free glass flux of the enamel is made easily by milhng the Substrate glass (float glass). The gloss and smoothness of the laser-fired enamel are comparable to the conventionally fired enamel. The absence of PbO and the high firing temperatures are very advantageous to the chemical resistance of the enamels, meaning excellent dishwasher durability
Computer Vision Tool for Detection, Mapping and Fault Classification of PV Modules in Aerial IR Videos
Increasing deployment of photovoltaics (PV) plants demands for cheap and fast
inspection. A viable tool for this task is thermographic imaging by unmanned
aerial vehicles (UAV). In this work, we develop a computer vision tool for the
semi-automatic extraction of PV modules from thermographic UAV videos. We use
it to curate a dataset containing 4.3 million IR images of 107842 PV modules
from thermographic videos of seven different PV plants. To demonstrate its use
for automated PV plant inspection, we train a ResNet-50 to classify ten common
module anomalies with more than 90 % test accuracy. Experiments show that our
tool generalizes well to different PV plants. It successfully extracts PV
modules from 512 out of 561 plant rows. Failures are mostly due to an
inappropriate UAV trajectory and erroneous module segmentation. Including all
manual steps our tool enables inspection of 3.5 MW p to 9 MW p of PV
installations per day, potentially scaling to multi-gigawatt plants due to its
parallel nature. While we present an effective method for automated PV plant
inspection, we are also confident that our approach helps to meet the growing
demand for large thermographic datasets for machine learning tasks, such as
power prediction or unsupervised defect identification
Assessment of string performance using self‐referencing method in comparison to performance ratio
In this study, we introduce a weather data independent self-referencing algorithm for performance analyses of PV power stations based on monitoring data. We introduce this method as an alternative to standard performance ratio analyses, in the case that no meteorological data are available. The self-referencing algorithm utilizes the string with maximum output as a reference. The output of other strings is set in relation to the corresponding value of the reference string, resulting in a quantity we call self-referencing ratio SR. We benchmark this ratio against the established performance ratio, which requires solar irradiance data for calculation. Based on a 9 MWp PV power station with 1,719 strings, we show that we are able to quantify, locate, and visualize the performance of each string in the PV power station. About 10% of the strings are underperforming; most of them are located in the bottom rows of the mounting racks. Shading from adjacent mounting racks is a possible explanation for this finding. Furthermore, IR-images and voltage data indicate a frequent occurrence of strings with a reduced number of modules. An underperformance of less than 2% is challenging to detect, due to the uncertainty range of the chosen dataset
Statistical EL-Image Evaluation for Describing the Degradation of PV-Modules after a Hailstorm
El-images are used to visualize defects in solar modules. Quantitative and comparable figures to address and identify defective cells are not extracted from the images so far. We used a statistical approach to get three key figures from the images: defective status, power-relevant area, and cell degradation. For analysis several EL-images of the same module were necessary. To study the impact of wind loads on the crack structures, the modules were exposed to 23,300 mechanical loading cycles, mimicking wind loads up to 40 m/s. The evolution of the crack structures was studied based on the ELimages that were recorded occasionally. The image evaluation reveals that defective, power-reducing cells and nondefective cells can be distinguished. Cell changes could be identified. The impact on the module power was within the measurement accuracy
Case study on the dependency of the degradation rate on degradation modes and methodology using monitoring data
We present here a degradation study of a PV power plant consisting of several module pairs connecting each to a micro-inverter with monitoring. The modules comprise cell cracks, cell breakages and edge shunting. The impact of using different methodologies on the resulting relative yield loss rates is analyzed. It is demonstrated how these methods can be altered to circumvent missing irradiation sensor data generating a substitute for a relative degradation rate. The relative degradation rate of cell cracks is close to the reference with all methods and near to the detection limit which indicates little degradation for cell cracks beyond the material-inherent degradation. The modules with cell breakages and edge shunting show a higher relative yield loss of l-3 %/a depending on the method of analysis. This indicates quite a variation is possible depending on the method thus warranting caution when evaluating degradation rates or their substitutes. It was shown that cell breakages perform increasingly worse in summer leading to even higher but seasonal performance losses. Because of that, the relative yield loss can be much higher than just the relative loss in rated power due to probably averse temperature effects on defect performance. To better assess cell crack degradation, either longer observation periods or more precise methods are neede
A Self-Referencing Method for Detecting Underperforming Strings in MWp-PV-Generators
For optimization of operation and maintenance of PV power stations the knowledge (identification and localization) of poor-performing modules, strings and inverters is important. The developed self-referencing method analyzes monitoring data, with a particular focus on energy yield. First, a well-performing unit is identified, second, the other units are set into relation to the well-preforming unit. Without weather data and prior-knowledge of the PVsystem poor-performing units (strings, inverters) are identified in this fashion. Three case studies are presented. IRimages verified the failure types. Strings suffering from yield loss due to PID or bridged PV-modules where identified and the yield loss quantified. Strings in bottom rows perform less (-2.6%) than those in top rows. The noise is about 4%, which means, failures with power loss less than 4% are masked in the uncertainty level of the monitoring data, e. g. one bypassed substring, hot cells. Yield relevant module failures such as PID, bridged modules, several bypassed substrings, are identified and localized reliably using the self-referencing method
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