基于改进小波阈值函数和奇异值分解的电能质量扰动检测定位

为了在噪声环境下准确提取电能质量扰动特征，本文提出基于改进小波阈值函数去噪和奇异值分解的电能质量扰动检测定位方法。首先构建改进小波阈值函数对含噪电能质量扰动信号降噪，利用经验模态分解的信号频带划分能力，实现降噪后扰动信号各模态的有效分离，再采用希尔伯特变换提取各模态幅值、频率等特征信息，同时基于奇异值分解实现对扰动信号的起止时刻的有效定位。最后分别采用不同类型的电能质量扰动信号进行仿真实验，实验证明本文提出的算法不仅具有良好的抗噪性能，同时具有较高的定位准确度和良好的鲁棒性。

Power quality disturbance detection and location based on new threshold function and singular value decomposition

In order to extract disturbance features accurately in noisy environment, a power quality disturbance detection and location algorithm based on improved wavelet threshold function denoising and singular value decomposition is proposed. The improved wavelet threshold function is used to denoise the noisy power quality disturbance signal. The frequency band division ability of empirical mode decomposition is used to separate the various modes of the disturbance signal after denoising. Hilbert transform is used to extract the characteristic information such as the amplitude and frequency of each mode. Meanwhile, the effective location of the start and stop time of the disturbance signal is realized by the principle of singular value decomposition. The simulation results of different kinds of disturbance signals show the effectiveness of the algorithm. The experiments show that the proposed algorithm not only has good anti-noise performance, but also has high positioning accuracy and good robustness.