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...

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Autores principales:	Meiya Dong, Jumin Zhao, Deng-ao Li, Biaokai Zhu, Sihai An, Zhaobin Liu
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/fbabc48ef247450082f7ee0fa800ae9b
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spelling	oai:doaj.org-article:fbabc48ef247450082f7ee0fa800ae9b2021-11-10T01:03:24ZISEE: Industrial Internet of Things perception in solar cell detection based on edge computing1550-147710.1177/15501477211050552https://doaj.org/article/fbabc48ef247450082f7ee0fa800ae9b2021-11-01T00:00:00Zhttps://doi.org/10.1177/15501477211050552https://doaj.org/toc/1550-1477The 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.Meiya DongJumin ZhaoDeng-ao LiBiaokai ZhuSihai AnZhaobin LiuSAGE PublishingarticleElectronic computers. Computer scienceQA75.5-76.95ENInternational Journal of Distributed Sensor Networks, Vol 17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 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
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.
format	article
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
title	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_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_sort	isee: industrial internet of things perception in solar cell detection based on edge computing
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/fbabc48ef247450082f7ee0fa800ae9b
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ISEE: Industrial Internet of Things perception in solar cell detection based on edge computing

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