| Autor: |
Cai Jiecong, Guo Xutao, Chen Siyi |
| Jazyk: |
English<br />French |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
E3S Web of Conferences, Vol 423, p 01008 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2267-1242 |
| DOI: |
10.1051/e3sconf/202342301008 |
| Popis: |
The hot spot effect of photovoltaic panel refers to the local heating phenomenon caused by the photovoltaic panel being covered, which not only seriously affects the power generation efficiency of photovoltaic panel, but also is one of the most important factors threatening the service life of photovoltaic panel. In this paper, an edge computing system was designed to detect hot spot effect based on real-time sensing data such as current, voltage and illuminance. The system consists of three parts: data acquisition side, data processing side and data display side. The hot spot detection algorithm model based on machine learning is deployed on the edge side, which can detect the degree of hot spot effect and locate the hot spot according to the sensor data of each photovoltaic panel in real time. Additionally, this system could push the data to the cloud management platform and each user terminal to realize remote operation and maintenance. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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