Figure - available from: EPJ Photovoltaics
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X-component of the magnetic field Bx of a solar cell without busbar defects as heatmaps (a) and as a line plot perpendicular to the busbars (b) comparing the simulation and the experiment.
Source publication
Renewable energies have an increasing share in the energy supply. In order to ensure the security of this supply, the reliability of the systems is therefore increasingly important. In photovoltaic modules or in manufacturing, defective solar cells due to broken busbars, cross-connectors or faulty solder joints must be detected and repaired quickly...
Citations
... By generating labeled simulation data, a neural network can be trained using this data, enabling the trained model to analyze the measured data. This approach has proven successful in various applications, such as detecting broken busbars in solar cells [10], where simulation data generated through finite element method simulations of the current-induced magnetic field was utilized. ...
... Optimization of the training process can be achieved by refining either the training dataset or the object detection algorithm itself. For the training dataset, the inclusion of a small subset of real measurements could prove beneficial, as has been successfully demonstrated in the detection of busbars in photovoltaic modules [10]. This approach might also help to overcome the limitations of Magpylib in simulating real materials. ...
Magnetic field measurements play a vital role in various industries, particularly in the detection of cracks using magnetic field images, also known as magnetic field leakage testing. This paper presents an approach to automate the extraction of crack signals in magnetic field imaging by using neural networks. The proposed method relies on simulation-based training using the lightweight Python library Magpylib to calculate the three-dimensional static magnetic field of permanent magnets with surface defects. This approach has numerous advantages. It allows control of training data set variance by tuning simulation input parameters such as sample magnetization, measurement parameters, and defect properties to cover a wide range of cracks in size and position. Starting data acquisition before system operation allows investigating potential changes in sample shape or measurement parameters. Importantly, simulation-based data generation eliminates the need for physical measurements, leading to significant time savings. The study presents and discusses results obtained on two different ferromagnetic samples with surface cracks, a hollow cylinder and a steel sheet.
... These defects in solar cell can be evaluated by several ways depending on the nature and origin of the defects. Some common measurement tools such as dark current measurements, capacitance-voltage measurements, photoluminescence, electroluminescence, and I-V characteristics measurements have been used to examine the defects [48][49][50]. This paper also investigates the potential of Cu 2 ZnSnS 4 (CZTS) as a solar absorber material for photovoltaic applications, given its ecofriendly, earth-abundant, and low-cost nature [32,33]. ...
Copper zinc tin sulfide solar cell (CZTS), Cu2ZnSnS4-based solar cells have shown promising conversion efficiency because of their ease of variation in configurations. In this work, the architecture of a ZnO–Al/i–ZnO/n–CdS/CZTS/Mo solar cell was optimized by using Silvaco Atlas simulation software. In this simulation study, the thickness and defect density of the CZTS layer has been varied to achieve the highest efficiency of 26.58%, with Isc = 36.64 A and Voc = 0.909 V at a defect density of 1.8 × 10¹² cm⁻³. Increase in the layer thickness of CZTS improves the photon absorption and cell efficiency. This study has evidenced the impact of defect density on the absorber layer, including photo-generation rate, recombination rate, and solar cell efficiency. By optimizing the device parameters, it has achieved a fill factor of 79.74% under AM 1.5 illumination, demonstrating the potential for low-cost, highly efficient CZTS solar cells.
