基于经验小波变换和 WOA-CNN 的柔性直流电网故障检测

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Journal of Electrical Engineering & Technology Pub Date : 2024-09-14 DOI:10.1007/s42835-024-02038-9
Yan-Fang Wei, Ping Yang, Zhan-Ye Yang, Peng Wang, Xiao-Wei Wang
{"title":"基于经验小波变换和 WOA-CNN 的柔性直流电网故障检测","authors":"Yan-Fang Wei, Ping Yang, Zhan-Ye Yang, Peng Wang, Xiao-Wei Wang","doi":"10.1007/s42835-024-02038-9","DOIUrl":null,"url":null,"abstract":"<p>Flexible DC grid solves the disadvantages of high line loss and small transmission capacity of traditional AC grid, but it still has the problems of difficult to extract characteristic signals and fault diagnosis. To solve this problem, a fault detection method based on empirical wavelet transform (EWT) with multiscale fuzzy entropy (MFE) and Whale algorithm optimization with convolutional neural network (WOA-CNN) is proposed. Firstly, EWT is used to decompose the fault line mode voltage signal and obtain the fault component. Then, the correlation coefficient of each component is calculated, and the components with more feature information are reconstructed. The MFE value of the reconstructed signal under different faults is calculated. Finally, the fault feature quantity is input into WOA-CNN for classification. A large number of experiments demonstrate that this method has strong anti-interference ability and high accuracy, and can reliably detect line fault under different fault types, fault positions and transition resistance conditions. Its accuracy is significantly improved comparing with CNN, PSO-CNN, K-means clustering, PSO-SVM and BP neural network, with an average of 99.5834%.</p>","PeriodicalId":15577,"journal":{"name":"Journal of Electrical Engineering & Technology","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fault Detection of Flexible DC Grid Based on Empirical Wavelet Transform and WOA-CNN\",\"authors\":\"Yan-Fang Wei, Ping Yang, Zhan-Ye Yang, Peng Wang, Xiao-Wei Wang\",\"doi\":\"10.1007/s42835-024-02038-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Flexible DC grid solves the disadvantages of high line loss and small transmission capacity of traditional AC grid, but it still has the problems of difficult to extract characteristic signals and fault diagnosis. To solve this problem, a fault detection method based on empirical wavelet transform (EWT) with multiscale fuzzy entropy (MFE) and Whale algorithm optimization with convolutional neural network (WOA-CNN) is proposed. Firstly, EWT is used to decompose the fault line mode voltage signal and obtain the fault component. Then, the correlation coefficient of each component is calculated, and the components with more feature information are reconstructed. The MFE value of the reconstructed signal under different faults is calculated. Finally, the fault feature quantity is input into WOA-CNN for classification. A large number of experiments demonstrate that this method has strong anti-interference ability and high accuracy, and can reliably detect line fault under different fault types, fault positions and transition resistance conditions. Its accuracy is significantly improved comparing with CNN, PSO-CNN, K-means clustering, PSO-SVM and BP neural network, with an average of 99.5834%.</p>\",\"PeriodicalId\":15577,\"journal\":{\"name\":\"Journal of Electrical Engineering & Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Electrical Engineering & Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s42835-024-02038-9\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electrical Engineering & Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s42835-024-02038-9","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

柔性直流电网解决了传统交流电网线损高、输电容量小的缺点,但仍存在特征信号提取难、故障诊断难等问题。为解决这一问题,本文提出了一种基于经验小波变换(EWT)与多尺度模糊熵(MFE)和鲸鱼算法优化与卷积神经网络(WOA-CNN)的故障检测方法。首先,利用 EWT 对故障线路模式电压信号进行分解,得到故障分量。然后,计算各分量的相关系数,重建特征信息较多的分量。计算重建信号在不同故障下的 MFE 值。最后,将故障特征量输入 WOA-CNN 进行分类。大量实验证明,该方法具有较强的抗干扰能力和较高的准确度,能在不同故障类型、故障位置和过渡电阻条件下可靠地检测线路故障。与 CNN、PSO-CNN、K-means 聚类、PSO-SVM 和 BP 神经网络相比,其准确率明显提高,平均达到 99.5834%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
Fault Detection of Flexible DC Grid Based on Empirical Wavelet Transform and WOA-CNN

Flexible DC grid solves the disadvantages of high line loss and small transmission capacity of traditional AC grid, but it still has the problems of difficult to extract characteristic signals and fault diagnosis. To solve this problem, a fault detection method based on empirical wavelet transform (EWT) with multiscale fuzzy entropy (MFE) and Whale algorithm optimization with convolutional neural network (WOA-CNN) is proposed. Firstly, EWT is used to decompose the fault line mode voltage signal and obtain the fault component. Then, the correlation coefficient of each component is calculated, and the components with more feature information are reconstructed. The MFE value of the reconstructed signal under different faults is calculated. Finally, the fault feature quantity is input into WOA-CNN for classification. A large number of experiments demonstrate that this method has strong anti-interference ability and high accuracy, and can reliably detect line fault under different fault types, fault positions and transition resistance conditions. Its accuracy is significantly improved comparing with CNN, PSO-CNN, K-means clustering, PSO-SVM and BP neural network, with an average of 99.5834%.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Electrical Engineering & Technology
Journal of Electrical Engineering & Technology ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
4.00
自引率
15.80%
发文量
321
审稿时长
3.8 months
期刊介绍: ournal of Electrical Engineering and Technology (JEET), which is the official publication of the Korean Institute of Electrical Engineers (KIEE) being published bimonthly, released the first issue in March 2006.The journal is open to submission from scholars and experts in the wide areas of electrical engineering technologies. The scope of the journal includes all issues in the field of Electrical Engineering and Technology. Included are techniques for electrical power engineering, electrical machinery and energy conversion systems, electrophysics and applications, information and controls.
期刊最新文献
Parameter Solution of Fractional Order PID Controller for Home Ventilator Based on Genetic-Ant Colony Algorithm Fault Detection of Flexible DC Grid Based on Empirical Wavelet Transform and WOA-CNN Aggregation and Bidding Strategy of Virtual Power Plant Power Management of Hybrid System Using Coronavirus Herd Immunity Optimizer Algorithm A Review on Power System Security Issues in the High Renewable Energy Penetration Environment
×
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