Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 436-438.

• Big Data & Data Mining • Previous Articles     Next Articles

Vertical Analysis Based on Fault Data of Running Smart Meter

LIU Zi-yi1, LIU Qing1, WANG Chong1, WANG Ji-meng1, WANG Yue1, LIU Jin-shuo2, YIN Ze-hao2   

  1. State Grid Tianjin Electric Power Company Electric Power Research Institute,Tianjin 300000,China1;
    School of Cyber Science and Engineering,Wuhan University,Wuhan 430076,China2
  • Online:2019-06-14 Published:2019-07-02

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Abstract: As the main tool of electricity measurement and economic settlement,the failure rate of smart meter is directly related to the national economy and livelihood of the masses.This paper devised a vertical analysis model of fault data of running smart meter.The model can analyze the operation failure rate data of smart meters from different manufacturers and batches.The model firstly cleans the useless data,then carries out linear regression analysis on the basic data items,and gets the fault data and changing rate of the failure rate of each batch,which are utilized to do the cluster to evaluate the stability of the factory quality.The method and the result of the model can assess the quality of the batch of the smart meter,and can be beneficial to estimate the quality of factory.

Key words: Cluster, Multilayer model, Regression analysis, Smart meter

CLC Number: 

  • TM934
[1]董力通,周原冰,李蒙.智能电网对智能电表的要求及产业发展建议.能源技术经济,2010,22(1):15-17.
唐涛涛.电能表的误差发生分析与解决办法.现代测量与实验室管理,2011(3):13-15.
O’Connor,Kleyner.Guide for selecting and using reliability predictions based on IEEE 1413..http://www.doc88.com/p-90425856345.html.
王慧芳,杨荷娟,何奔腾,等.输变电设备状态故障率模型改进分析.电力系统自动化.2011,35(16):27-31.
顾伟,褚建新.基于故障统计模型的可修系统维修周期预测法.机械强度,2000,22(1):22-27.
GB 17215.911-200×/IEC/TR 62059-11-2002,电量计量设备. 可靠性.第11部分:一般概念..http://www.tsinfo.js.cn/wine/index/gbtdetails.aspx?A100=IEC/TR%2062059-11-2002.
GB 17215.911-200×/IEC/TR 62059-31-1-2008,电能测量设备--可靠性--第31-1部分:加速可靠性测试--提高温度和湿度..http://www.tsinfo.js.cn/inquiry/gbtdetails.aspx?A100=IEC%2062059-31-1-2008.
徐瑞.多层线性模型在高一物理学习影响因素分析中的应用.昆明:云南师范大学,2009.
刘红云,孟庆茂.纵向数据分析方法.心理科学进展,2003,11(5):586-592.
孙吉贵,刘杰,赵连宇.聚类算法研究.软件学报,2008,19(1):48-61.
李瑞莹,康锐.基于ARMA模型的故障率预测方法研究.系统工程与电子技术,2008,30(8):1588-1591.
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