基于经验小波变换的电力系统谐波检测方法

吴建章; 梅飞; 陈畅; 潘益; 李陶然; 郑建勇

引用本文:	吴建章,梅飞,陈畅,等.基于经验小波变换的电力系统谐波检测方法[J].电力系统保护与控制,2020,48(6):136-143.
	WU Jianzhang,MEI Fei,CHEN Chang,et al.Harmonic detection method in power system based on empirical wavelet transform[J].Power System Protection and Control,2020,48(6):136-143

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基于经验小波变换的电力系统谐波检测方法
吴建章¹,梅飞²,陈畅¹,潘益¹,李陶然¹,郑建勇¹
(1.东南大学电气工程学院，江苏南京 210096;2.河海大学能源与电气学院，江苏南京 211100)

摘要:

针对噪声干扰下的稳态以及暂态谐波检测问题，首次提出一种基于经验小波变换的电力系统谐波检测方法。首先利用经验小波变换从电力谐波信号中提取出一组具有紧支撑频谱的调幅-调频分量，实现各次谐波与基波信号的分离。接着对分离出的谐波分量进行Hilbert变换，从而获取各次谐波的幅值和频率检测参数以及暂态谐波的扰动起止时刻。对多类谐波信号的仿真结果表明，所提方法有效避免了传统Hilbert-Huang变换存在的模态混叠问题，即使在低信噪比下也能实现多频谐波信号的自适应分解，在确保各类参数检测结果精度的同时，兼具良好的噪声鲁棒性和检测实时性。

关键词: 经验小波变换 Hilbert变换调幅-调频分量噪声谐波检测

DOI：10.19783/j.cnki.pspc.190470

分类号:

基金项目:国家电网公司科技项目资助(52199918000C)

Harmonic detection method in power system based on empirical wavelet transform

WU Jianzhang¹,MEI Fei²,CHEN Chang¹,PAN Yi¹,LI Taoran¹,ZHENG Jianyong¹

(1. School of Electrical Engineering, Southeast University, Nanjing 210096, China;2. College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract:

In order to detect stationary and transient harmonics under noise interference, an empirical wavelet transform based harmonic detection method for power system is proposed for the first time. Firstly, the empirical wavelet transform is used to extract a set of AM-FM components from power harmonic signals, and the separation of each harmonic and fundamental wave signals is realized. Then, the separated harmonic components are transformed by Hilbert transform to obtain the amplitude and frequency parameters of each harmonic and the start-end time of transient disturbance. The simulation results of multi-class harmonic signals show that the proposed method avoids the mode aliasing problem existing in traditional Hilbert-Huang transform effectively and realizes the adaptive decomposition of multi-frequency harmonic signals even at low signal-to-noise ratio. It ensures the accuracy of detection results of various parameters and has good noise robustness and real-time detection. This work is supported by Science and Technology Project of State Grid Corporation of China (No. 52199918000C).

Key words: empirical wavelet transform (EWT) Hilbert transform amplitude modulated-frequency modulated component noise harmonic detection

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