双旋转最大集结卷积神经网络用于太阳能电池裂检测
Sharmarke Hassan1, Mahmoud Dhimish2
1Photovoltaics Laboratory, School of Physics, Engineering and Technology, University of York, York, YO10 5DD, UK. smh600@york.ac.uk.
Scientific reports
|July 9, 2023
概括
本研究介绍了一种使用卷积神经网络 (CNN) 的太阳能电池裂检测系统,用于识别光伏 (PV) 电池中的缺陷. 先进的系统准确评估太阳能电池质量,提高光伏组装效率.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 光伏 (PV) 太阳能电池的质量控制对于高效的能源生产至关重要.
- 需要自动缺陷检测系统来提高光伏组装单元的可靠性.
- 现有的检测太阳能电池缺陷的方法,如裂和潜在诱导退化 (PID) 有局限性.
研究的目的:
- 为光伏 (PV) 装配线开发和验证太阳能电池裂检测系统.
- 评估不同卷积神经网络 (CNN) 架构的性能,以识别缺陷.
- 评估系统检测各种缺陷的能力,包括裂,微裂,PID和阴影区域.
主要方法:
- 使用了四种不同的卷积神经网络 (CNN) 架构.
- 在太阳能电池缺陷分析中使用电发光 (EL) 成像技术.
- 通过热测试和现实世界缺陷场景验证了系统的预测.
主要成果:
- 达到了高达99.5%的太阳能电池接受率.
- 证明了对缺陷的准确预测,例如阴影区域和微裂.
- 拟议的CNN模型在缺陷检测准确性方面表现优于之前的研究.
结论:
- 开发的太阳能电池裂检测系统是评估光伏电池状况的宝贵工具.
- 该系统可以显著减少光伏组件中的缺陷电池,提高整体效率.
- 这项研究对提高光伏行业的质量和性能具有重大影响.
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