基于改进经验小波变换的局部放电荧光信号去噪

由于现场环境的复杂性,局部放电(partial discharge,PD)检测伴随着大量噪声干扰,易出现PD漏报与误报现象,影响电力设备后续运维工作。文中采用荧光光纤PD检测法,提出了基于谱峭度和改进经验小波变换(empirical wavelet transform,EWT)的自适应PD荧光信号去噪算法。首先利用快速谱峭度图确定荧光信号傅里叶频谱的紧支撑区域边界,随后对含噪荧光信号进行EWT分解并获得荧光信号所在的有用信号分量,最后对有用信号分量采用小波阈值法去除残留噪声,得到去噪后的PD荧光信号。利用该方法对仿真荧光信号进行去噪分析,并将去噪结果与经验模态分解-小波变换(empirical mode decomposition-wavelet transform,EMD-WT)法和EWT法进行对比,结果表明,该方法在信噪比、均方根误差和归一化相关系数等指标方面都有所提升,证明了该方法具有良好的去噪效果。此外,实测信号的去噪结果表明,该方法的降噪率优于EMD-WT法和EWT法,具有较好的噪声抑制能力。

Denoising of partial discharge fluorescence signals based on improved empirical wavelet transform

Due to the complexity of the operation environment,partial discharge (PD) detection is accompanied by a large amount of noise interference,resulting in the phenomenon of missed and false alarms,which affects the subsequent operation and maintenance of power equipment. In this paper,an adaptive denoising algorithm for PD fluorescence signal based on spectral kurtosis and improved empirical wavelet transform (EWT) is proposed by fluorescence fiber PD detection method. Firstly,the fast kurtogram is used to determine the compact support boundary of the Fourier spectrum of the fluorescence signal,and then the EWT is used to decompose the noise-containing fluorescence signal to obtain the useful signal components where the fluorescence signal is located. Finally,the wavelet threshold method is used to remove the residual noise from the useful signal components to obtain the denoised PD fluorescence signal. This proposed method is used to carry out denoising analysis on simulated fluorescent signals. The de-noising results are compared with the empirical mode decomposition-wavelet transform (EMD-WT) and the EWT method,which show that the method in this paper improves the signal-to-noise ratio,root-mean-square error and normalized correlation coefficient,proving that the method has good denoising effect. In addition,the denoising results of the measured signals demonstrate that this method in this paper has a higher noise reduction rate than the EMD-WT method or the EWT method does,as well as superior noise suppression capacity.