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文章摘要
基于卷积神经网络的光伏阵列污染报警系统
Solar panels pollution alarm system based on convolution neural network
Received:October 21, 2019  Revised:October 21, 2019
DOI:10.19753/j.issn1001-1390.2020.07.009
中文关键词: 光伏阵列  卷积神经网络  报警系统  网站
英文关键词: Photovoltaic system  convolution neural network  alarm system  website
基金项目:2015东莞市引进第三批创新科研团队项目(2017360004004);广州市南沙区科技计划项目(2017CX009);广东省交通厅科技项目(科技-2017-02-041);福建省自然科学基金项目(2018J01541)
Author NameAffiliationE-mail
XUE Jiaxiang* South China University of Technology mejiaxue@scut.edu.cn 
CHEN Haifeng South China University of Technology 1119741880@qq.com 
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中文摘要:
      针对依靠人工检查光伏阵列是否需要清洗的低效率,机器人定期清洗又会造成资源浪费的问题,文章创新的提出一种基于卷积神经网络(convolution neural network,CNN)的光伏阵列污染报警系统。该系统通过采集受污染和未受污染的光伏阵列图像作为训练集训练CNN神经网络,将训练好的模型嵌入网站后端,再定期采集光伏阵列图像,通过CNN神经网络模型识别是否受到污染,当光伏阵列受到污染时由前端显示报警信息。实验结果表明:该系统识别精度达97.6%,系统工作稳定,具有较强的实用价值。
英文摘要:
      In view of the inefficient manual cleaning of solar panels and the automatic cleaning robot will cause waste of resources. This paper proposes a solar panels pollution alarm system based on convolution neural network. Collecting contaminated and uncontaminated images of solar panels as training set to train convolution neural network, embedding the model into the website. The website displays the alarm message. The experimental results show that the classification accuracy of the system is 97.6%, the system worls stably and has strong practical value.#$NLKeywords: Photovoltaic system; convolution neural network; alarm system; website
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