Detection and identification of ferroresonance

Heba Abu Sharbain, A. Osman, A. El-Hag
{"title":"Detection and identification of ferroresonance","authors":"Heba Abu Sharbain, A. Osman, A. El-Hag","doi":"10.1109/ICMSAO.2017.7934904","DOIUrl":null,"url":null,"abstract":"This paper presents an artificial intelligent based method to detect ferroresonance. The proposed detection method utilizes wavelet transform combined with artificial neural network to detect ferroresonance. Using this method, ferroresonance can be identified and differentiated. The results show that the proposed procedure is effective in identifying ferroresonance from other transients such as capacitor switching. Moreover, they indicate that the used neural network model has an acceptable precision in the recognition of ferroresonance and by adjusting the right parameters, the highest precision is achieved.","PeriodicalId":265345,"journal":{"name":"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICMSAO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMSAO.2017.7934904","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

This paper presents an artificial intelligent based method to detect ferroresonance. The proposed detection method utilizes wavelet transform combined with artificial neural network to detect ferroresonance. Using this method, ferroresonance can be identified and differentiated. The results show that the proposed procedure is effective in identifying ferroresonance from other transients such as capacitor switching. Moreover, they indicate that the used neural network model has an acceptable precision in the recognition of ferroresonance and by adjusting the right parameters, the highest precision is achieved.
铁磁共振的检测与鉴定
提出了一种基于人工智能的铁磁共振检测方法。该检测方法利用小波变换结合人工神经网络对铁磁共振进行检测。利用该方法可以识别和区分铁磁共振。结果表明,该方法可以有效地从电容开关等瞬变中识别铁磁谐振。结果表明,所使用的神经网络模型在铁共振识别中具有可接受的精度,并且通过调整适当的参数可以达到最高的精度。
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 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