Second generation wavelet transform for data denoising in PD measurement
Second generation wavelet transform for data denoising in PD measurement
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DOI：
10.1109/tdei.2007.4401237
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发表时间：
2007-12
影响因子：
3.1
通讯作者：
Xiaodi Song;Chengke Zhou;D. Hepburn;Guobin Zhang;M. Michel
中科院分区：
文献类型：
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作者：
Xiaodi Song;Chengke Zhou;D. Hepburn;Guobin Zhang;M. Michel
局部放电的检测与诊断在电力设备状态监测中得到了广泛的应用。在实际应用中，局部放电的分析和检测常常受到信号中噪声的影响。最近的研究表明，离散小波变换（DWT）是有效的提取局部放电脉冲从严重的噪声。然而，一个缺点是，在存在强噪声的情况下，在阈值化之后，DWT不能再现准确的PD脉冲幅度和脉冲形状。提出了一种改进的第二代小波变换（SGWT）算法，用于从电噪声中提取局部放电脉冲信号。本文首先介绍了广义小波变换的基本理论和结构，并与离散小波变换进行了比较。然后将该方法应用于模拟和真实的世界PD数据。实验结果表明，SGWT能显著提高局部放电去噪的效果。
Detection and diagnosis of partial discharge (PD) activity has been widely adopted in electrical plant condition monitoring. Analysis and detection of PD in practical applications is often hampered by noise in the signal. Recent research has shown that the discrete wavelet transform (DWT) is effective in extracting PD pulses from severe noise. One disadvantage, however, is that DWT does not reproduce accurate PD pulse magnitude and pulse shape after thresholding in the presence of strong noise. This paper presents the application of the second generation wavelet transform (SGWT), as an improved algorithm, to extraction of PD pulse from electrical noise. The paper begins with the description of the fundamental theory and structure of SGWT analysis and comparisons with DWT. The method is then applied to both simulated and real world PD data. Results prove that SGWT can significantly improve the effectiveness of PD denoising.