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首页> 外文期刊>Electric Power Components and Systems >Support Vector Machine-based Denoising Technique for Removal of White Noise in Partial Discharge Signal
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Support Vector Machine-based Denoising Technique for Removal of White Noise in Partial Discharge Signal

机译：基于支持向量机的去噪技术，去除局部放电信号中的白噪声

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摘要

Partial discharge (PD) measurement has emerged as a dominant investigative tool for condition monitoring of insulation in high voltage equipment. In general, PD signals are severely polluted by several noises like white noise, random noise, discrete spectral interferences (DSI). The challenge lies with removing these noises from PD signal effectively by preserving the signal features. In this article, support vector machine (SVM) based denoising technique has been proposed for the removal of white noise from PD signal. The proposed SVM technique retains the edge of the original signal efficiently and also pseudo Gibbs phenomenon does not exist with SVM technique. In order to evaluate the effectiveness of the proposed method, artificially simulated PD signal mixed with white noise and the measured PD readings are considered. For the purpose of comparison, other denoising techniques such as fast Fourier transform (FFT), discrete wavelet transform (DWT), and translation invariant wavelet transform (TIWT) are also considered. The results reveal that, SVM based denoising technique shows better performance in terms of higher signal to noise ratio, signal reconstruction error ratio, cross correlation coefficient and reduction in noise level, mean square error, and waveform distortion.
机译：局部放电（PD）测量已成为高压设备绝缘状态监测的主要研究工具。通常，PD信号会受到白噪声，随机噪声，离散频谱干扰（DSI）等几种噪声的严重污染。挑战在于如何通过保留信号特征有效地从PD信号中消除这些噪声。在本文中，提出了一种基于支持向量机（SVM）的降噪技术，用于从PD信号中去除白噪声。所提出的支持向量机技术有效地保留了原始信号的边缘，并且支持向量机技术不存在伪吉布斯现象。为了评估该方法的有效性，考虑了人工模拟的PD信号与白噪声的混合以及测得的PD读数。为了进行比较，还考虑了其​​他降噪技术，例如快速傅立叶变换（FFT），离散小波变换（DWT）和平移不变小波变换（TIWT）。结果表明，基于SVM的降噪技术在较高的信噪比，信号重构误差率，互相关系数以及噪声水平降低，均方误差和波形失真方面表现出更好的性能。

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