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Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning

机译：用隔离开发模型传输深度学习自动检测红外图像中的光伏模块缺陷

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摘要

With the rising use of photovoltaic and ongoing installation of large-scale photovoltaic systems worldwide, the automation of photovoltaic monitoring methods becomes important, as manual/visual inspection has limited applications. This research work deals with automatic detection of photovoltaic module defects in Infrared images with isolated deep learning and develop-model transfer deep learning techniques. An Infrared images dataset containing infrared images of normal operating and defective modules is collected and used to train the networks. The dataset is obtained from Infrared imaging performed on normal operating and defective photovoltaic modules with lab induced defects. An isolated learned model is trained from scratch using a light convolutional neural network design that achieved an average accuracy of 98.67%. For transfer learning, a base model is first developed (pre-trained) from electroluminescence images dataset of photovoltaic cells and then fine-tuned on infrared images dataset, that achieved an average accuracy of 99.23%. Both frameworks require low computation power and less time; and can be implemented with ordinary hardware. They also maintained real time prediction speed. The comparison shows that the develop-model transfer learning technique can help to improve the performance. In addition, we reviewed different kind of defects detectable from infrared imaging of photovoltaic modules, that can help in manual labelling for identifying different defect categories upon access to new huge data in future studies. Last of all, the presented frameworks are applied for experimental testing and qualitative evaluation.
机译：随着光伏和持续安装在全球大型光伏系统的使用中，光伏监测方法的自动化变得重要，因为手动/目视检查具有有限的应用。该研究工作涉及自动检测红外图像的光伏模块缺陷，具有隔离的深度学习和开发模型传输深层学习技术。收集包含正常操作和缺陷模块的红外图像的红外图像数据集并用于培训网络。数据集是从正常操作和有缺陷的光伏模块上进行的红外成像获得，具有实验室诱导的缺陷。使用光卷积神经网络设计从划痕培训孤立的学习模型，实现了98.67％的平均精度。对于转移学习，首先从光伏电​​池的电致发光图像数据集开发（预先培训的基础模型，然后在红外图像数据集上进行微调，这实现了99.23％的平均精度。这两个框架都需要低计算能力和更少的时间;并且可以用普通的硬件实现。它们还保持了实时预测速度。比较表明，发展模型传输学习技术可以有助于提高性能。此外，我们审查了从光伏模块的红外成像中检测到不同类型的缺陷，这可以帮助在访问未来研究中获取新数据时识别不同的缺陷类别。最后，所提出的框架适用于实验测试和定性评估。

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  • 来源
    《Solar Energy》 |2020年第3期|175-186|共12页
  • 作者

    Akram M. Waqar; Li Guiqiang; Jin Yi; Chen Xiao; Zhu Changan; Ahmad Ashfaq;

  • 作者单位

    Univ Sci & Technol China Dept Precis Machinery & Instrumentat Hefei 230026 Anhui Peoples R China;

    Univ Hull Sch Engn Kingston Upon Hull HU6 7RX N Humberside England;

    Univ Sci & Technol China Dept Precis Machinery & Instrumentat Hefei 230026 Anhui Peoples R China;

    Univ Sci & Technol China State Key Lab Fire Sci Hefei 230026 Anhui Peoples R China;

    Univ Sci & Technol China Dept Precis Machinery & Instrumentat Hefei 230026 Anhui Peoples R China;

    Univ Sci & Technol China Dept Precis Machinery & Instrumentat Hefei 230026 Anhui Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Photovoltaic (PV) modules; Thermography; Automatic defect detection; Infrared images; Isolated deep learning; Develop-model transfer deep learning;

    机译：光伏（PV）模块;热成像;自动缺陷检测;红外图像;隔离深度学习;发展模型转移深度学习;

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