Research and Application of Verification Error Data Processing of Electricity Meter Based on Grubbs Criterion

Xie Guang-cheng, C. Wenli, Liu Xingzhi, Zheng Ke, Zou Bo, S. Hongliang
{"title":"Research and Application of Verification Error Data Processing of Electricity Meter Based on Grubbs Criterion","authors":"Xie Guang-cheng, C. Wenli, Liu Xingzhi, Zheng Ke, Zou Bo, S. Hongliang","doi":"10.1109/ICSGEA.2019.00012","DOIUrl":null,"url":null,"abstract":"In electricity meter automatic verification process, sometimes abnormal verification error data was given due to the drastic fluctuations of load power and frame drop. Traditional processing method increases measurement and takes average value as the basic data of electricity meter, which is invalid when the abnormity error data is far away from maximum permissible error. For this reason, a new method of determination of basic error is put forward, which uses Grubbs criterion to recognize and eliminate abnormal error data. Take the 13 verification error data of triphase electricity meter as analysis sample, Grubbs criterion is used to eliminate abnormal error data in the data processing, and further calculate the verification basic error of electricity meter. The results show that the method can more accurately reflect the basic error of electricity meter, and improve the verification efficiency, decrease the waste of verification source.","PeriodicalId":201721,"journal":{"name":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"210 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Smart Grid and Electrical Automation (ICSGEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSGEA.2019.00012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In electricity meter automatic verification process, sometimes abnormal verification error data was given due to the drastic fluctuations of load power and frame drop. Traditional processing method increases measurement and takes average value as the basic data of electricity meter, which is invalid when the abnormity error data is far away from maximum permissible error. For this reason, a new method of determination of basic error is put forward, which uses Grubbs criterion to recognize and eliminate abnormal error data. Take the 13 verification error data of triphase electricity meter as analysis sample, Grubbs criterion is used to eliminate abnormal error data in the data processing, and further calculate the verification basic error of electricity meter. The results show that the method can more accurately reflect the basic error of electricity meter, and improve the verification efficiency, decrease the waste of verification source.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于Grubbs准则的电能表检定误差数据处理研究与应用
在电能表自动检定过程中,有时会由于负载功率的剧烈波动和机架下降而给出异常的检定误差数据。传统的处理方法是增加测量量,以平均值作为电能表的基本数据,当异常误差数据远离最大允许误差时,这种方法是无效的。为此,提出了一种新的基本误差确定方法,即利用Grubbs准则识别和消除异常误差数据。以三相电能表的13个检定误差数据为分析样本,采用Grubbs判据剔除数据处理中的异常误差数据,进一步计算电能表检定基本误差。结果表明,该方法能较准确地反映电能表的基本误差,提高了检定效率,减少了检定源的浪费。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Summary of Studies on Bilingual Comparable Corpus Research and Application of Verification Error Data Processing of Electricity Meter Based on Grubbs Criterion Exploration of Clipped Barrier Silicon Carbide Schottky Diode Human Face Expression Recognition Based on Deep Learning-Deep Convolutional Neural Network Technical Research on High Power Silicon Carbide Schottky Barrier Diode
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1