徐长宝1,古庭赟1,高云鹏2?覮,吴聪2,龙秋风1,周金2.基于改进小波阈值函数和变分模态分解的 电能质量扰动检测[J].湖南大学学报:自然科学版,2020,(6):77~86
基于改进小波阈值函数和变分模态分解的 电能质量扰动检测
Power Quality Disturbance Detection Based on Improved Wavelet Threshold Function and Variational Mode Decomposition
  
DOI:
中文关键词:  电能质量  变分模态分解  希尔伯特变换  奇异值分解  改进小波阈值函数
英文关键词:power quality  Variational Mode Decomposition(VMD)  Hilbert transform  singular value decomposition  improved wavelet threshold function
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作者单位
徐长宝1,古庭赟1,高云鹏2?覮,吴聪2,龙秋风1,周金2 (1. 贵州电网有限责任公司 电力科学研究院贵州 贵阳 550001 2. 湖南大学 电气与信息工程学院湖南 长沙 410082) 
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中文摘要:
      针对强噪声环境下扰动特征提取困难的问题,提出一种基于改进小波阈值函数和变分模态分解的电能质量扰动检测算法. 采用改进小波阈值函数滤除电能质量扰动信号的噪声,通过傅里叶变换确定预设尺度,再基于变分模态分解准确地求出电能质量扰动信号的各个本征模态函数,结合Hilbert变换和奇异值分解分别求解每个本征模态函数的振幅、频率、起止时间等特征量,并据此搭建PXI和LabVIEW结合的电能质量扰动检测平台. 分别采用单一扰动、复合扰动和电网实际扰动数据验证本文算法的准确性与有效性,相比现有经验模态分解和集合经验模态分解,本文提出算法不仅具有抗模态混叠和虚假分量的能力,且在强噪声环境下仍具有较高的准确性和鲁棒性.
英文摘要:
      In order to extract the disturbance features accurately in strong noisy environment, a power quality disturbance detection and location algorithm based on improved wavelet threshold function denoising and Variational Mode Decomposition(VMD) is proposed. The improved wavelet threshold function is used to denoise the noisy power quality disturbance signal. The default scale can be determined by the Fourier transform. This paper uses the variational mode decomposition to decompose signals into some intrinsic modes. 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. A power quality disturbance detection platform based on PXI and LabVIEW is also built based on the above algorithm. The accuracy and effectiveness of the proposed algorithm are verified by single disturbance, complex disturbance and actual disturbance data. Compared with the existing empirical mode decomposition and ensemble empirical mode decomposition, the proposed algorithm not only has the ability of resisting modal aliasing and false components, but also has high accuracy and robustness under the environment of strong noise.
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