Automatic segmentation method for voltage sag detection and characterization

Huang Wen-xi, Xia Xian-yong, Jin Yun-ling, Yao Dong-Fang
{"title":"Automatic segmentation method for voltage sag detection and characterization","authors":"Huang Wen-xi, Xia Xian-yong, Jin Yun-ling, Yao Dong-Fang","doi":"10.1109/ICHQP.2018.8378821","DOIUrl":null,"url":null,"abstract":"Although characterization of voltage sag is an essential part of voltage sag studies, the way that taking magnitude and duration as acknowledged basic characteristics cannot describe sag characteristics versus time. Hence automatic segmentation, which divides monitoring data sequence into segments, and characterization algorithm are proposed in this paper. The difficulty that how to divide segment automatically is overcome through two-stage segmentation algorithm based on singular value decomposition method. Then multi-dimension characteristics such as magnitude, duration, phase-angle jump, sag type and so on can be calculated. Hundreds of sag events data including measured in field and synthetic are utilized to validate the effectiveness and reliability of proposed method. Moreover, the detection and characterization algorithm are ported to installed monitors and backstage data center with C programming, timesaving and practical get validated.","PeriodicalId":6506,"journal":{"name":"2018 18th International Conference on Harmonics and Quality of Power (ICHQP)","volume":"8 1","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 18th International Conference on Harmonics and Quality of Power (ICHQP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICHQP.2018.8378821","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Although characterization of voltage sag is an essential part of voltage sag studies, the way that taking magnitude and duration as acknowledged basic characteristics cannot describe sag characteristics versus time. Hence automatic segmentation, which divides monitoring data sequence into segments, and characterization algorithm are proposed in this paper. The difficulty that how to divide segment automatically is overcome through two-stage segmentation algorithm based on singular value decomposition method. Then multi-dimension characteristics such as magnitude, duration, phase-angle jump, sag type and so on can be calculated. Hundreds of sag events data including measured in field and synthetic are utilized to validate the effectiveness and reliability of proposed method. Moreover, the detection and characterization algorithm are ported to installed monitors and backstage data center with C programming, timesaving and practical get validated.
电压凹陷检测与表征的自动分割方法
虽然电压凹陷的表征是电压凹陷研究的重要组成部分,但以幅度和持续时间作为公认的基本特征的方法无法描述电压凹陷随时间的特征。为此,本文提出了将监测数据序列分割成多个片段的自动分割算法和表征算法。采用基于奇异值分解的两阶段分割算法,克服了自动分割分割的困难。进而计算出地震震级、持续时间、相角跳变、凹陷类型等多维特征。利用现场和人工合成的数百个凹陷事件数据,验证了该方法的有效性和可靠性。并将检测与表征算法用C语言编程移植到已安装的监视器和后台数据中心,省时、实用得到了验证。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信