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首页> 外文期刊>Science, Measurement & Technology, IET >Wavelet base selection for de-noising and extraction of partial discharge pulses in noisy environment
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Wavelet base selection for de-noising and extraction of partial discharge pulses in noisy environment

机译：小波基选择用于噪声环境中局部放电脉冲的去噪和提取

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摘要

Wavelet-based de-noising is used to separate partial discharge (PD) signals from the noises resulting from measurement circuits or the surrounding environment. PD de-noising by using the wavelet shrinkage method is capable of separating the noise component to some extent, but the selection of the wavelet base has a remarkable effect on the de-noising results. The wavelet base is directly related to the distortion of the PD waveform and quality of the de-noising process. Although there are applications on PD noise separation in the literature, the selection of the wavelet base, which affects the evaluation of the PD characteristics, is still challenging. Instead of using correlation-based wavelet base selection for de-noising PD data, in this study a novel wavelet base selection method based on the most informative sub-band energy and entropy for separating noise from PD pulses is introduced and successfully applied to raw data obtained from the PD measurement set-up. The advantage of the proposed method is that the wavelet base selection solution is automatic and independent of the original noise-free pulse waveform. This study shows that the proposed method is useful for the extraction of noisy PD pulses by describing the basic discharge parameters such as discharge amplitude and the duration and time of occurrence more clearly.
机译：基于小波的降噪用于将局部放电（PD）信号与测量电路或周围环境产生的噪声分开。利用小波收缩法进行局部放电去噪能够在一定程度上分离噪声分量，但是小波基的选择对去噪效果有显着影响。小波基与PD波形的失真和去噪处理的质量直接相关。尽管在文献中有关于PD噪声分离的应用，但是影响PD特性评估的小波基的选择仍然具有挑战性。代替使用基于相关的小波基选择对PD数据进行降噪，本研究引入了一种基于信息量最大的子带能量和熵的新小波基选择方法，用于将PD脉冲中的噪声分离出来，并将其成功地应用于原始数据从PD测量设置中获得。该方法的优点是小波基选择解决方案是自动的，并且与原始的无噪声脉冲波形无关。这项研究表明，通过更清晰地描述基本放电参数（例如放电幅度以及发生的持续时间和时间），该方法可用于提取有噪声的PD脉冲。

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