Harmonic / Interharmonic Spectrum Separation Algorithm Based on Fast Independent Component Analysis

Wenlong DU; Honggeng YANG; Xiaoyang MA

doi:10.7500/AEPS20190812007

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Harmonic / Interharmonic Spectrum Separation Algorithm Based on Fast Independent Component Analysis

Application Research | 更新时间：2025-06-16

- Harmonic / Interharmonic Spectrum Separation Algorithm Based on Fast Independent Component Analysis
- Automation of Electric Power Systems Vol. 44, Issue 13, Pages: 115-122(2020)
- 作者机构：
  
  四川大学电气工程学院，四川省成都市 610065
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China
- DOI：10.7500/AEPS20190812007
  CLC：
- Received：12 August 2019，
  
  Revised：2020-01-14，
  
  Published Online：19 May 2020，
  
  Published：10 July 2020
- 稿件说明：
移动端阅览
杜文龙,杨洪耕,马晓阳.基于快速独立分量分析的谐波/间谐波频谱分离算法[J].电力系统自动化,2020,44(13):115-122.

DU Wenlong,YANG Honggeng,MA Xiaoyang.Harmonic / Interharmonic Spectrum Separation Algorithm Based on Fast Independent Component Analysis[J].Automation of Electric Power Systems,2020,44(13):115-122.
杜文龙,杨洪耕,马晓阳.基于快速独立分量分析的谐波/间谐波频谱分离算法[J].电力系统自动化,2020,44(13):115-122. DOI： 10.7500/AEPS20190812007.

DU Wenlong,YANG Honggeng,MA Xiaoyang.Harmonic / Interharmonic Spectrum Separation Algorithm Based on Fast Independent Component Analysis[J].Automation of Electric Power Systems,2020,44(13):115-122. DOI： 10.7500/AEPS20190812007.

摘要

在非同步采样条件下，若电网采样信号中谐波和间谐波相邻，会出现严重的频谱干涉问题，且无法识别出信号中实际频率成分。针对以上问题，提出了一种基于快速独立分量分析（FastICA）的频谱分离算法测量谐波和间谐波参数。首先构建了多频率成分模型，将频谱中的谱线表示为多个频率成分分量的叠加，然后利用FastICA算法和最小二乘法得到频率成分参数，最终实现了对相邻多频率成分的测量。仿真结果表明，该算法可以在需求谱线数较少的情况下准确识别频率成分并保持较好的测量精度，且具有一定的抗噪能力。

Abstract

Under the condition of asynchronous sampling

if the harmonics and interharmonics in the sampling signals of power grid are adjacent

serious spectrum interference will occur. The actual frequency components of the signals cannot be identified. Therefore

a spectrum separation algorithm based on fast independent component analysis (FastICA) is proposed to measure harmonic and interharmonic parameters. Firstly

the model of multi-frequency components is constructed. Spectral lines in the spectrum are represented as the superposition of multiple frequency components. Secondly， frequency component parameters are obtained by using FastICA and least squares method. Finally

the measurement of adjacent multi-frequency components is realized. The simulation results show that the algorithm can accurately identify the frequency components with a small number of required spectral lines

and has good measurement accuracy and certain anti-noise ability.

关键词

Keywords

references

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