DocumentCode :
3660186
Title :
An ultra-short-term power prediction model based on machine vision for distributed photovoltaic system
Author :
Bao Guanjun;Tian Liubin;Cai Shibo;Tong Jianjun;Zhang Linwei;Xu Fang
Author_Institution :
Key Laboratory of E&
fYear :
2015
Firstpage :
1148
Lastpage :
1152
Abstract :
Distributed photovoltaic(PV) system is easily affected by the cloud cluster moving in the sky because of its small scale. The instantaneous shelter caused by the moving cloud cluster may lead to the output power of photovoltaic system fluctuation violently. The cloud cluster monitoring device was designed, which aims to track the solar trajectory and take photos of the cloud cluster. The centroid position feature model and shape feature model were established based on image-based processing algorithms. They can forecast the position and shape of cloud cluster in the near future. And an ultra-short-term power prediction model based on machine vision for distributed photovoltaic system was established. Simulation results show that the established model can track the position of cloud cluster in the sky, and predict the shape-to-be of cloud cluster.
Keywords :
"Clouds","Photovoltaic systems","Sun","Solar radiation","Cameras","Shape","Predictive models"
Publisher :
ieee
Conference_Titel :
Information and Automation, 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/ICInfA.2015.7279459
Filename :
7279459
Link To Document :
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