光伏系统
可靠性(半导体)
卷积神经网络
可靠性工程
质量保证
计算机科学
质量(理念)
汽车工程
探测器
实时计算
人工智能
工程类
电气工程
电信
功率(物理)
运营管理
物理
外部质量评估
哲学
认识论
量子力学
作者
Sharmarke Hassan,Mahmoud Dhimish
标识
DOI:10.1016/j.renene.2023.119389
摘要
Detecting cracks in solar photovoltaic (PV) modules plays an important role in ensuring their performance and reliability. The development of convolutional neural networks (CNNs) has introduced a game-changing dimension in the detection of defects in PV modules. This paper proposes an automated defect detection method for PV, by leveraging custom-designed CNN to accurately analyse electroluminescence (EL) images, identifying defects such as cracks, mini-cracks, potential induced degradation (PID), and shaded areas. The proposed system achieves a high level of validation accuracy of 98.07%, reducing manual inspection demands, enhancing quality standards, and saving costs. The system was validated in a case study for PV installations faulty with PID, where it identified all defective modules with a high degree of precision of 96.6%, surpassing existing methods. This methodology holds promise for revolutionizing PV industry quality control, improving module reliability, and supporting sustainable solar energy growth.
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