ISEE: Industrial Internet of Things perception in solar cell detection based on edge computing
The photovoltaic industry is a strategic and sunrise industry with international competitive advantages. Driven by policy guidance and market demand, the new energy industry represented by the photovoltaic industry has been a significant emerging industry in developing the national economy and peopl...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2021-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501477211050552 |
| _version_ | 1797719894509223936 |
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| author | Meiya Dong Jumin Zhao Deng-ao Li Biaokai Zhu Sihai An Zhaobin Liu |
| author_facet | Meiya Dong Jumin Zhao Deng-ao Li Biaokai Zhu Sihai An Zhaobin Liu |
| author_sort | Meiya Dong |
| collection | DOAJ |
| description | The photovoltaic industry is a strategic and sunrise industry with international competitive advantages. Driven by policy guidance and market demand, the new energy industry represented by the photovoltaic industry has been a significant emerging industry in developing the national economy and people’s livelihood. Stable photovoltaic power generation capacity supply is a critical issue in the photovoltaic industry. With the popularization of industrial Internet technology and Internet of things technology, more and more academic and industrial circles begin to introduce new technologies to provide the latest research results and solutions for the photovoltaic industry. Electroluminescence is a standard detection method for photovoltaic production in the application of solar energy production. This method uses human vision to detect whether the solar silicon unit is defective. In this article, due to the three core pain points in traditional electroluminescence detection: low efficiency of offline identification, low accuracy and accuracy of data detection, and no online diagnosis and prediction, we carry out ISEE research based on edge computing unit. ISEE uses the edge device to collect the real-time video image of the solar panel through the camera. Then it uses the powerful neural network processing unit module of the edge computing unit, combined with the convolutional neural network algorithm transplanted to the edge, to detect the defects of solar panels in real time. It completes the research on intelligent detection of photovoltaic power generation production defects based on the Internet of Things. After a large number of experimental design verification, ISEE effectively improves the automation degree and identification accuracy in the production and detection process of solar photovoltaic cells and reduces the cost of operation and maintenance. The accuracy rate reaches 93.75%, which has significant theoretical research significance and practical application value. |
| first_indexed | 2024-03-12T09:11:25Z |
| format | Article |
| id | doaj.art-fbabc48ef247450082f7ee0fa800ae9b |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T09:11:25Z |
| publishDate | 2021-11-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-fbabc48ef247450082f7ee0fa800ae9b2023-09-02T15:00:29ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772021-11-011710.1177/15501477211050552ISEE: Industrial Internet of Things perception in solar cell detection based on edge computingMeiya Dong0Jumin Zhao1Deng-ao Li2Biaokai Zhu3Sihai An4Zhaobin Liu5College of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaCollege of Information and Computer, Taiyuan University of Technology, Taiyuan, ChinaNetwork Security Department, Shanxi Police College, Taiyuan, ChinaEngineering Department, GengDan Institute of Beijing University of Technology, Beijing, ChinaSchool of Computer engineering, Suzhou Vocational University, Suzhou, ChinaThe photovoltaic industry is a strategic and sunrise industry with international competitive advantages. Driven by policy guidance and market demand, the new energy industry represented by the photovoltaic industry has been a significant emerging industry in developing the national economy and people’s livelihood. Stable photovoltaic power generation capacity supply is a critical issue in the photovoltaic industry. With the popularization of industrial Internet technology and Internet of things technology, more and more academic and industrial circles begin to introduce new technologies to provide the latest research results and solutions for the photovoltaic industry. Electroluminescence is a standard detection method for photovoltaic production in the application of solar energy production. This method uses human vision to detect whether the solar silicon unit is defective. In this article, due to the three core pain points in traditional electroluminescence detection: low efficiency of offline identification, low accuracy and accuracy of data detection, and no online diagnosis and prediction, we carry out ISEE research based on edge computing unit. ISEE uses the edge device to collect the real-time video image of the solar panel through the camera. Then it uses the powerful neural network processing unit module of the edge computing unit, combined with the convolutional neural network algorithm transplanted to the edge, to detect the defects of solar panels in real time. It completes the research on intelligent detection of photovoltaic power generation production defects based on the Internet of Things. After a large number of experimental design verification, ISEE effectively improves the automation degree and identification accuracy in the production and detection process of solar photovoltaic cells and reduces the cost of operation and maintenance. The accuracy rate reaches 93.75%, which has significant theoretical research significance and practical application value.https://doi.org/10.1177/15501477211050552 |
| spellingShingle | Meiya Dong Jumin Zhao Deng-ao Li Biaokai Zhu Sihai An Zhaobin Liu ISEE: Industrial Internet of Things perception in solar cell detection based on edge computing International Journal of Distributed Sensor Networks |
| title | ISEE: Industrial Internet of Things perception in solar cell detection based on edge computing |
| title_full | ISEE: Industrial Internet of Things perception in solar cell detection based on edge computing |
| title_fullStr | ISEE: Industrial Internet of Things perception in solar cell detection based on edge computing |
| title_full_unstemmed | ISEE: Industrial Internet of Things perception in solar cell detection based on edge computing |
| title_short | ISEE: Industrial Internet of Things perception in solar cell detection based on edge computing |
| title_sort | isee industrial internet of things perception in solar cell detection based on edge computing |
| url | https://doi.org/10.1177/15501477211050552 |
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