An electricity stealing method of anti-high-frequency pulsed magnetic field based on SVD and wavelet transform

阙华坤; 林国龙; 曹云飞; 冯小峰; 江泽涛; 李健; 范竞敏

doi:10.3969/j.issn.1009-1831.2022.03.015

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An electricity stealing method of anti-high-frequency pulsed magnetic field based on SVD and wavelet transform

更新时间：2026-03-05

- An electricity stealing method of anti-high-frequency pulsed magnetic field based on SVD and wavelet transform
- Vol. 24, Issue 3, (2022)
- 作者机构：
  
  1. 广东电网有限责任公司计量中心,广州,518049
  2. 广东工业大学自动化学院,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2022.03.015
  CLC：
- Published：2022
- 稿件说明：
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阙华坤, 林国龙, 曹云飞, et al. An electricity stealing method of anti-high-frequency pulsed magnetic field based on SVD and wavelet transform[J]. 2022, 24(3).
DOI：

阙华坤, 林国龙, 曹云飞, et al. An electricity stealing method of anti-high-frequency pulsed magnetic field based on SVD and wavelet transform[J]. 2022, 24(3). DOI： 10.3969/j.issn.1009-1831.2022.03.015.

摘要

面对更隐蔽的高频脉冲磁场窃电方式，根据磁场信号进行反窃电检测会由于噪声影响而失效。针对这个问题，提出将奇异值分解（singular value decomposition，SVD）和小波变换结合的方法应用到防窃电装置中，在检测中去除噪声分离出窃电信号。首先将带有噪声的磁场混合信号进行快速傅立叶变换（fast Fourier transform，FFT）求出功率谱，引入经典阈值判断出窄带噪声的个数，并以此确定奇异值分解的有效秩阶次。然后对混合信号SVD分解，将窄带噪声对应的的奇异值置零后重构信号，最后利用小波变换去除随机噪声的影响。仿真和实验结果表明，此方法能有效应用到窃电检测中，并通过与 FFT 阈值滤波法、经验模态分解（empirical modedecomposition，EMD）法对比，所提方法抑制效果更突出。

Abstract

Faced with the more insidious electricity stealing methods of high-frequency pulsed magnetic field

anti-theft detection based on magnetic field signals would fail due to noise. Aiming at this problem

a method combining SVD and wavelet transform is applied to anti-theft devices and proposed to remove noise and separate stolen electricity signal in detection. Firstly

the noisy magnetic field mixed signal is transformed by FFT to obtain the power spectrum

and the classical threshold is introduced to determine the number of narrow-band noises and the effective rank order of the SVD.Then the mixed signal is SVD decomposed

the singular value corresponding to the narrow-band noise is reset to zero

and then the signal is reconstructed. Finally

wavelet transform is used to remove the influence of random noise. Simulation and experimental results show that

this method can be effectively applied to the detection of electrical stealing. Compared with FFT threshold filtering method and EMD method

the suppression effect of the proposed method is more prominent.

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