基于改进VMD的有源电力滤波器谐波自适应检测

Wenjie Huang, Rongkun Wang, Yujia Zhuang, Zhixin Wang, Quankai Du
{"title":"基于改进VMD的有源电力滤波器谐波自适应检测","authors":"Wenjie Huang, Rongkun Wang, Yujia Zhuang, Zhixin Wang, Quankai Du","doi":"10.1109/CIEEC54735.2022.9845851","DOIUrl":null,"url":null,"abstract":"Harmonic detection accuracy is the key factor affecting the performance of active power filter (APF). As an effective harmonic detection method, any complex signal can be adaptively decomposed into several intrinsic mode functions (IMFs) with a certain bandwidth by Variational Mode Decomposition (VMD). Aiming at the problems that VMD will be affected under the background of strong noise and the parameters are difficult to determine, an adaptive harmonic detection method based on twice VMD is proposed in this paper. Firstly, the proposed method uses VMD with a fixed decomposition layer A of 2 for denoising. Secondly, the VMD parameters are optimized from the perspective of IMF energy, and the second VMD decomposition is performed on the denoised signal. The simulation results show that, compared with EMD, EEMD and VMD, the proposed method can not only effectively reduce the fundamental extraction error, but also be suitable for aperiodic and non-stationary signals. It shows that the harmonic detection accuracy of APF can be effectively improved by this method.","PeriodicalId":416229,"journal":{"name":"2022 IEEE 5th International Electrical and Energy Conference (CIEEC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive Harmonic Detection of Active Power Filter based on Improved VMD\",\"authors\":\"Wenjie Huang, Rongkun Wang, Yujia Zhuang, Zhixin Wang, Quankai Du\",\"doi\":\"10.1109/CIEEC54735.2022.9845851\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Harmonic detection accuracy is the key factor affecting the performance of active power filter (APF). As an effective harmonic detection method, any complex signal can be adaptively decomposed into several intrinsic mode functions (IMFs) with a certain bandwidth by Variational Mode Decomposition (VMD). Aiming at the problems that VMD will be affected under the background of strong noise and the parameters are difficult to determine, an adaptive harmonic detection method based on twice VMD is proposed in this paper. Firstly, the proposed method uses VMD with a fixed decomposition layer A of 2 for denoising. Secondly, the VMD parameters are optimized from the perspective of IMF energy, and the second VMD decomposition is performed on the denoised signal. The simulation results show that, compared with EMD, EEMD and VMD, the proposed method can not only effectively reduce the fundamental extraction error, but also be suitable for aperiodic and non-stationary signals. It shows that the harmonic detection accuracy of APF can be effectively improved by this method.\",\"PeriodicalId\":416229,\"journal\":{\"name\":\"2022 IEEE 5th International Electrical and Energy Conference (CIEEC)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 5th International Electrical and Energy Conference (CIEEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIEEC54735.2022.9845851\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 5th International Electrical and Energy Conference (CIEEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIEEC54735.2022.9845851","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

谐波检测精度是影响有源电力滤波器性能的关键因素。变分模态分解(VMD)是一种有效的谐波检测方法,可以将任何复杂信号自适应地分解为若干具有一定带宽的本征模态函数(IMFs)。针对强噪声背景下VMD受影响且参数难以确定的问题,提出了一种基于二次VMD的自适应谐波检测方法。首先,该方法采用固定分解层a为2的VMD进行去噪。其次,从IMF能量的角度对VMD参数进行优化,对去噪后的信号进行二次VMD分解;仿真结果表明,与EMD、EEMD和VMD相比,该方法不仅能有效降低基波提取误差,而且适用于非周期和非平稳信号。结果表明,该方法能有效提高有源滤波器的谐波检测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Harmonic Detection of Active Power Filter based on Improved VMD
Harmonic detection accuracy is the key factor affecting the performance of active power filter (APF). As an effective harmonic detection method, any complex signal can be adaptively decomposed into several intrinsic mode functions (IMFs) with a certain bandwidth by Variational Mode Decomposition (VMD). Aiming at the problems that VMD will be affected under the background of strong noise and the parameters are difficult to determine, an adaptive harmonic detection method based on twice VMD is proposed in this paper. Firstly, the proposed method uses VMD with a fixed decomposition layer A of 2 for denoising. Secondly, the VMD parameters are optimized from the perspective of IMF energy, and the second VMD decomposition is performed on the denoised signal. The simulation results show that, compared with EMD, EEMD and VMD, the proposed method can not only effectively reduce the fundamental extraction error, but also be suitable for aperiodic and non-stationary signals. It shows that the harmonic detection accuracy of APF can be effectively improved by this method.
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
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