Implementation of a Power Quality signal classification system using wavelet based energy distribution and neural network

P. Sebastian, Pramod Antony DSa
{"title":"Implementation of a Power Quality signal classification system using wavelet based energy distribution and neural network","authors":"P. Sebastian, Pramod Antony DSa","doi":"10.1109/ICPACE.2015.7274935","DOIUrl":null,"url":null,"abstract":"This paper presents a classification system based on Wavelet Transform for normal Power Quality disturbances. The parameter used for the classification algorithm was energy distribution of detailed coefficient of Wavelet Transform up to 10 leveles of decomposition. Wavelet Transform parameters are effective in classification of disturbance waveforms. Artificial Neural Network was used for the classification purpose which gave satisfactory results. And the algorithm developed in MATLAB was interfaced with Data Acquisition devices to check its accuracy for online classification purpose.","PeriodicalId":6644,"journal":{"name":"2015 International Conference on Power and Advanced Control Engineering (ICPACE)","volume":"47 1","pages":"157-161"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Power and Advanced Control Engineering (ICPACE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPACE.2015.7274935","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

This paper presents a classification system based on Wavelet Transform for normal Power Quality disturbances. The parameter used for the classification algorithm was energy distribution of detailed coefficient of Wavelet Transform up to 10 leveles of decomposition. Wavelet Transform parameters are effective in classification of disturbance waveforms. Artificial Neural Network was used for the classification purpose which gave satisfactory results. And the algorithm developed in MATLAB was interfaced with Data Acquisition devices to check its accuracy for online classification purpose.
基于小波能量分布和神经网络的电能质量信号分类系统的实现
提出了一种基于小波变换的电能质量扰动分类系统。分类算法使用的参数为10级分解前小波变换细节系数的能量分布。小波变换参数是一种有效的扰动波形分类方法。采用人工神经网络进行分类,取得了满意的分类效果。并将MATLAB开发的算法与数据采集设备进行接口,验证其在线分类的准确性。
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