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基于快速独立分量分析的谐波/间谐波频谱分离算法
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四川大学电气工程学院,四川省成都市 610065

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基金项目:

国家自然科学基金资助项目(51477105); 四川大学博士后交叉学科创新培育项目(0030304153003)。


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

School of Electrical Engineering, Sichuan University, Chengdu 610065, China

Fund Project:

This work is supported by National Natural Science Fundation of China (No. 51477105) and Sichuan University Post-doctoral Interdisciplinary Innovation Cultivation Project (No. 0030304153003).

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    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:

    With the condition of asynchronous sampling, if the harmonics and interharmonics in the power grid sampling signal are adjacent ,serious frequency spectrum interference problems will occur. The actual frequency components cannot be identified in the signals. Aiming at the problems, a spectrum separation and measurement algorithm based on fast independent component analysis (FastICA) is proposed to measure harmonic and interharmonic parameters. Firstly, the model of multi-frequency components is built. Spectral lines in the frequency spectrum are represented as a 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 result shows that the algorithm can accurately identify the frequency components with a small number of required spectral lines, and has good measurement accuracy and anti-noise ability.

    表 3 测量结果Table 3 Measurement results
    表 4 实验信号参数Table 4 Parameters of experimental signal
    表 5 实验信号测量结果Table 5 Measurement results of experimental signals
    图1 基波和间谐波的混合频谱Fig.1 Mixed spectrum of fundamental and inter-harmonics
    图2 基于FastICA的频谱分离算法流程图Fig.2 Flow chart of spectrum separation algorithm based on FastICA
    图3 m=6的频谱分离结果Fig.3 Spectrum separation results of m=6
    图4 不同m值的误差比较Fig.4 Comparison of errors with different values of m
    图5 不同SNR时的参数误差Fig.5 Parameter errors with different SNR
    图 FastICA求解步骤图Fig. Solving step diagram of FastICA
    图 仿真信号频谱图Fig. Spectrogram of simulation signal
    图 去除基波后的频谱图Fig. Spectrogram excluding the fundamental component
    图 电流信号时域图Fig. Time domain diagram of current signal
    图 实验方案框图Fig. Block diagram of experimental scheme
    表 1 仿真信号参数Table 1 Parameters of simulation signal
    表 2 基波测量结果Table 2 Measurement results of fundamental
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引用本文

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/OL].电力系统自动化,http://doi.org/10.7500/AEPS20190812007.
DU Wenlong,YANG Honggeng,MA Xiaoyang.Harmonic / Interharmonic Spectrum Separation Algorithm Based on Fast Independent Component Analysis[J/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20190812007.

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  • 收稿日期:2019-08-12
  • 最后修改日期:2020-05-14
  • 录用日期:2020-02-08
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