基于关联规则挖掘算法的光伏发电设备故障预警的研究
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- 英文论文题名：Research on fault early warning of photovoltaic power generation equipment based on Association Rule Mining Algorithm
- 论文作者：叶进 ; 宋冬冬 ; 朱健
- 英文论文作者：Ye jin ; Song dongdong ; Zhujian ; School of computer and electronic information ; Guangxi University
- 年：2016
- 作者机构：广西大学计算机与电子信息学院;
- 论文关键词：光伏发电设备 ; 故障预警 ; Apriori算法 ; 数据挖掘
- 英文论文关键词：photovoltaic ; faultearly warning ; Apriori algorithm ; data mining
- 会议召开时间：2016-11-25
- 会议录名称：广西计算机学会2016年学术年会论文集
- 语种：中文
- 分类号：TM615;TP311.13
- 学会代码：GXJH
- 会议名称：广西计算机学会2016年学术年会
- 会议地点：中国广西南宁
- 主办单位：广西计算机学会
- 学会名称：广西计算机学会
- 页数：5
- 文件大小：139k
- 原文格式：D
- 会议级别：地方
摘要
提高光伏发电设备运行能力和对光伏设备故障进行准确的预警已经成为当前光伏电站发展的关键。将关联规则挖掘引入到故障诊断中,从数据本身出发去寻找故障规则,这样能有效的指导维修人员对设备进行故障定位和设备维护[10]。随着挖掘技术的发展,尤其在故障诊断领域中的研究,通过使用Apriori算法得到到各个故障数据及其变化量之间的强关联规则,从而使对故障的定位更加精准,时效性更高。
The key to the development of photovoltaic power station is to improve the operational capability of photovoltaic power generation equipment and to make the accurate warning of photovoltaic equipment fault. The association rules mining is introduced into the fault diagnosis, from the data itself to find the fault rules, so that it can effectively guide the maintenance personnel to the equipment fault location and equipment maintenance [10]. With the development of mining technology, especially in the field of fault diagnosis, to obtain strong association rules between the various fault data and its variation by using the Apriori algorithm, so that the location of the fault is more accurate and more efficient.
The key to the development of photovoltaic power station is to improve the operational capability of photovoltaic power generation equipment and to make the accurate warning of photovoltaic equipment fault. The association rules mining is introduced into the fault diagnosis, from the data itself to find the fault rules, so that it can effectively guide the maintenance personnel to the equipment fault location and equipment maintenance [10]. With the development of mining technology, especially in the field of fault diagnosis, to obtain strong association rules between the various fault data and its variation by using the Apriori algorithm, so that the location of the fault is more accurate and more efficient.
引文
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[2]王景丹,刘桂莲,孔波,董永超,龚晓伟.基于光伏电站的智能运维知识库的研究[J].电源技术,2016,08:1669‐1672.
[3]刘廷建,王自豪,郑朝霞.基于关联规则的数据挖掘在供水管网故障诊断中的运用[J].科技情报开发与经济,2005,15(14):150‐152.
[4]张文瑾,葛强,黄澄扬,等.基于BP神经网络的光伏阵列智能故障诊断[J].扬州大学学报(自然科学版),2015(1):32‐35.
[5]郑雅琼.基于数据挖掘的故障诊断方法研究[D].太原理工大学,2011.
[6]于博文.数据挖掘技术在故障诊断中的应用[D].东北石油大学,2015.
[7]Jia Wei Han,陈绍业.数据挖掘概念与技术[M].北京:机械工业出版社,2012
[8]Syafaruddin S,Karatepe E,Hiyama T.Controlling of artificial neural network for fault diagnosis of photovoltaic array[C]//International Conference on Intelligent System Application To Power Systems.2011:1‐6.
[9]Takashima T,Yamaguchi J,Ishida M.Disconnection detection using earth capacitance measurement in photovoltaic module string[J].Progress in Photovoltaics Research&Applications,2008,16(16):669‐677.
[10]Agrawal R,Imieliński T,Swami A.Mining association rules between sets of items in large databases[J].Acm Sigmod Record,1993,22(2):207‐216.