Adaptive detection and classificaion system for power quality disturbances

J. Sierra-Fernandez, J. D. L. de la Rosa, J. Palomares-Salas, A. Aguera-Perez, A. Jimenez-Montero
{"title":"Adaptive detection and classificaion system for power quality disturbances","authors":"J. Sierra-Fernandez, J. D. L. de la Rosa, J. Palomares-Salas, A. Aguera-Perez, A. Jimenez-Montero","doi":"10.1109/ICPEC.2013.6527713","DOIUrl":null,"url":null,"abstract":"This paper describes an intelligent measurement system for Power Quality (PQ) assessment. Computational guts are based in Higher Order Statistics (HOS) and the intelligent decision system is based in the Case-Base Reasoning (CBR) paradigm, which could re-configure its parameter according to the power net conditions. The power signal characterization is done using a sliding window procedure, and calculating the variance, the skewness and the kurtosis over the points inside the window. Those values are introduced in the CBR system and the signal state is returned. If the signal is healthy, the system study the current HOS values for substitute the normal considerations of the CBR system. This procedure returns a precision over the 90%.","PeriodicalId":176900,"journal":{"name":"2013 International Conference on Power, Energy and Control (ICPEC)","volume":"330 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Power, Energy and Control (ICPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPEC.2013.6527713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

This paper describes an intelligent measurement system for Power Quality (PQ) assessment. Computational guts are based in Higher Order Statistics (HOS) and the intelligent decision system is based in the Case-Base Reasoning (CBR) paradigm, which could re-configure its parameter according to the power net conditions. The power signal characterization is done using a sliding window procedure, and calculating the variance, the skewness and the kurtosis over the points inside the window. Those values are introduced in the CBR system and the signal state is returned. If the signal is healthy, the system study the current HOS values for substitute the normal considerations of the CBR system. This procedure returns a precision over the 90%.
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
电能质量扰动的自适应检测与分类系统
介绍了一种用于电能质量评估的智能测量系统。基于高阶统计量(HOS)的计算模型和基于案例推理(CBR)的智能决策系统,可以根据电网情况重新配置其参数。功率信号表征是使用滑动窗口过程完成的,并计算窗口内点的方差、偏度和峰度。这些值被引入CBR系统并返回信号状态。如果信号是健康的,系统研究当前的HOS值来代替CBR系统的正常考虑。这个过程返回超过90%的精度。
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
Cascaded seven level inverter with reduced number of switches using level shifting PWM technique Design, analysis and simulation of linear controller of a STATCOM for reactive power compensation on variation of DC link voltage Transmission line faults detection, classification, and location using Discrete Wavelet Transform New multilevel inverter topology with reduced number of switches using advanced modulation strategies Performance analysis of PV fed single phase T-source inverter
×
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