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

[Display omitted] •State-of-the-art framework is proposed for automatic defect detection in PV modules.•Infrared images dataset of normal operating and defective PV modules is collected.•Isolated and develop-model transfer deep learning frameworks are proposed.•Isolated & transfer learned method...

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Published in:	Solar energy Vol. 198; pp. 175 - 186
Main Authors:	Akram, M. Waqar, Li, Guiqiang, Jin, Yi, Chen, Xiao, Zhu, Changan, Ahmad, Ashfaq
Format:	Journal Article
Language:	English
Published:	New York Elsevier Ltd 01.03.2020 Pergamon Press Inc
Subjects:	Accuracy Artificial neural networks Automatic defect detection Automation Datasets Deep learning Defects Develop-model transfer deep learning Electroluminescence Infrared imagery Infrared images Infrared imaging Inspection Isolated deep learning Labeling Machine learning Modules Monitoring methods Neural networks Performance enhancement Photovoltaic (PV) modules Photovoltaic cells Photovoltaics Solar energy Thermography Transfer learning Isolated deep learning Automatic defect detection Develop-model transfer deep learning Photovoltaic (PV) modules Infrared images Thermography
ISSN:	0038-092X, 1471-1257
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Abstract	[Display omitted] •State-of-the-art framework is proposed for automatic defect detection in PV modules.•Infrared images dataset of normal operating and defective PV modules is collected.•Isolated and develop-model transfer deep learning frameworks are proposed.•Isolated & transfer learned methods give 98.67% and 99.23% accuracy respectively.•These frameworks are qualitatively evaluated with experimental testing. 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.
AbstractList	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. [Display omitted] •State-of-the-art framework is proposed for automatic defect detection in PV modules.•Infrared images dataset of normal operating and defective PV modules is collected.•Isolated and develop-model transfer deep learning frameworks are proposed.•Isolated & transfer learned methods give 98.67% and 99.23% accuracy respectively.•These frameworks are qualitatively evaluated with experimental testing. 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.
Author	Li, Guiqiang Akram, M. Waqar Chen, Xiao Zhu, Changan Ahmad, Ashfaq Jin, Yi
Author_xml	– sequence: 1 givenname: M. Waqar surname: Akram fullname: Akram, M. Waqar organization: Department of Precision Machinery and Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China – sequence: 2 givenname: Guiqiang surname: Li fullname: Li, Guiqiang email: guiqiang.li@hull.ac.uk organization: School of Engineering, University of Hull, Hull HU6 7RX, UK – sequence: 3 givenname: Yi surname: Jin fullname: Jin, Yi email: jinyi08@ustc.edu.cn organization: Department of Precision Machinery and Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China – sequence: 4 givenname: Xiao surname: Chen fullname: Chen, Xiao organization: State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, Anhui 230026, China – sequence: 5 givenname: Changan surname: Zhu fullname: Zhu, Changan organization: Department of Precision Machinery and Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China – sequence: 6 givenname: Ashfaq orcidid: 0000-0001-5559-043X surname: Ahmad fullname: Ahmad, Ashfaq organization: Department of Precision Machinery and Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China
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Keywords	Isolated deep learning Automatic defect detection Develop-model transfer deep learning Photovoltaic (PV) modules Infrared images Thermography
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Snippet	[Display omitted] •State-of-the-art framework is proposed for automatic defect detection in PV modules.•Infrared images dataset of normal operating and... With the rising use of photovoltaic and ongoing installation of large-scale photovoltaic systems worldwide, the automation of photovoltaic monitoring methods...
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SubjectTerms	Accuracy Artificial neural networks Automatic defect detection Automation Datasets Deep learning Defects Develop-model transfer deep learning Electroluminescence Infrared imagery Infrared images Infrared imaging Inspection Isolated deep learning Labeling Machine learning Modules Monitoring methods Neural networks Performance enhancement Photovoltaic (PV) modules Photovoltaic cells Photovoltaics Solar energy Thermography Transfer learning
Title	Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning
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