These results validate the effectiveness of PV-YOLOv12n in detecting critical PV panel defects, supporting its deployment in large-scale solar farm inspections.
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Efficient and intelligent surface defect detection of photovoltaic modules is crucial for improving the quality of photovoltaic modules and ensuring the reliable operation of large-scale
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Photovoltaic (PV) generation systems are susceptible to various types of faults. Our objective is to identify unusual operating conditions in a photovoltaic string using only the voltage and
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The deployment of solar photovoltaic (PV) panel systems, as renewable energy sources, has seen a rise recently. Consequently, it is imperative to implement efficient methods for the
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Automated defect detection in electroluminescence (EL) images of photovoltaic (PV) modules on production lines remains a significant challenge, crucial for replacing labor-intensive and
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The contribution of this paper is as follows: (1) Constructing a photovoltaic power grid fault recognition architecture based on improved GABP neural network and fuzzy system, achieving
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In photovoltaic (PV) cell inspection, electroluminescence (EL) imaging provides high spatial resolution for detecting various types of defects. The recent integration of EL imaging with
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This study proposed a fault detection technique to identify various defects in PV systems, working in both “off-grid” and “grid-tied” modes [62]. The created system employs PLC as the
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Abstract This paper presents a defect analysis and performance evaluation of photovoltaic (PV) modules using quantitative electroluminescence imaging (EL). The study analyzed three
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Detecting defects on photovoltaic panels using electroluminescence images can significantly enhance the production quality of these panels. Nonetheless, in the process of defect
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