Harmonic Detection Algorithm Based on Kaiser Window

Chen Li, Qing An
{"title":"Harmonic Detection Algorithm Based on Kaiser Window","authors":"Chen Li, Qing An","doi":"10.1109/TOCS50858.2020.9339684","DOIUrl":null,"url":null,"abstract":"Aiming at the problems of large-scale error such as spectrum leakage and non-synchronous sampling use the method of the Fast Fourier Transform (FFT) in the harmonic detection of power system, in this paper put forward the method Kaiser window with FFT and the way harmonic analysis of window function restoration coefficient for reducing amplitude error. The magnitude recovery coefficient of the window function is calculated by simulation, when the signal of Kaiser window analyzed by FFT can suppressed the spectral leakage and multiplied by the amplitude recovery coefficient of window function corresponding to value of β in order to get the amplitude value of each harmonic. The simulation results show that the method of window FFT analysis and amplitude recovery can be realized by using 20 harmonic signals, when β = 10, the amplitude error of Kaiser window is lower than that of triangular window, Hanning window and Blackman window; when β = 30, the harmonic amplitude error of Kaiser window is less than 4%. By comparing the magnitude error of others window functions and different β values, can reduced the amplitude error of harmonic analysis by improved the β value of Kaiser window.","PeriodicalId":373862,"journal":{"name":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"507 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TOCS50858.2020.9339684","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

Aiming at the problems of large-scale error such as spectrum leakage and non-synchronous sampling use the method of the Fast Fourier Transform (FFT) in the harmonic detection of power system, in this paper put forward the method Kaiser window with FFT and the way harmonic analysis of window function restoration coefficient for reducing amplitude error. The magnitude recovery coefficient of the window function is calculated by simulation, when the signal of Kaiser window analyzed by FFT can suppressed the spectral leakage and multiplied by the amplitude recovery coefficient of window function corresponding to value of β in order to get the amplitude value of each harmonic. The simulation results show that the method of window FFT analysis and amplitude recovery can be realized by using 20 harmonic signals, when β = 10, the amplitude error of Kaiser window is lower than that of triangular window, Hanning window and Blackman window; when β = 30, the harmonic amplitude error of Kaiser window is less than 4%. By comparing the magnitude error of others window functions and different β values, can reduced the amplitude error of harmonic analysis by improved the β value of Kaiser window.
基于Kaiser窗的谐波检测算法
针对快速傅里叶变换(FFT)方法在电力系统谐波检测中存在频谱泄漏、采样不同步等大范围误差问题,提出了基于FFT的Kaiser窗法和窗函数恢复系数谐波分析方法来减小振幅误差。当FFT分析的Kaiser窗口信号能够抑制频谱泄漏时,通过仿真计算窗函数的幅度恢复系数,并乘以β值对应的窗函数的幅度恢复系数,得到各谐波的幅度值。仿真结果表明,利用20个谐波信号可以实现窗口FFT分析和幅度恢复,当β = 10时,Kaiser窗口的幅度误差小于三角形窗口、Hanning窗口和Blackman窗口;当β = 30时,Kaiser窗的谐波幅度误差小于4%。通过比较其他窗函数和不同β值的幅值误差,可以通过改进Kaiser窗的β值来减小谐波分析的幅值误差。
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:604180095
Book学术官方微信