Abstract
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.
Highlights
By a new round of energy revolution, photovoltaic power generation has become the leading choice of new energy development[1] for human beings in the future
Our study introduces the cloud operation and maintenance system of solar photovoltaic power generation based on big data
We study the main problems by designing intelligent recognition systems of solar panels based on edge devices
Summary
By a new round of energy revolution, photovoltaic power generation has become the leading choice of new energy development[1] for human beings in the future. International Journal of Distributed Sensor Networks emissions by 2030 and achieve carbon neutrality by 2060; Germany will achieve 33% of renewable power generation in 2017; France plans to generate all its electricity from clean energy by 2050. Photovoltaic power generation plays the leading role in renewable energy. The power generation situation has many uncertainties, and it brings a lot of technical difficulties to large-scale photovoltaic grid-connected power generation forecasts. Accurate and efficient detection of solar panel defects and accurate prediction of short-term photovoltaic power generation and power generation has important theoretical significance and application value. The quality of the core solar panels directly determines the power generation efficiency and operation safety. How to quickly and accurately predict the defects in production is very important
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