基于随机奇异值分解的局部放电脉冲提取及去噪技术

A Partial Discharge Pulse Extraction and Denoising Technology Based on Random Singular Value Decomposition

针对低信噪比下局部放电信号易漏检与传统奇异值分解算法在进行局放脉冲提取时计算量大的问题,提出一种基于随机奇异值分解的局部放电脉冲提取及去噪方法。该方法能有效提取局放脉冲及去除白噪声,且相较于传统SVD脉冲提取计算所需时间更短,更具工程实用价值。首先,利用滑动短时数据窗截取原始局放信号片段,采用随机奇异值分解法计算最大奇异值,并与全局最优奇异值阈值进行比较,确定脉冲信号的起止点;然后,利用奇异值分解法结合局部最优奇异值阈值,去除提取信号的白噪声。通过对典型局放模拟脉冲进行实验,验证了该算法在脉冲提取时的执行效率优越性。在工频电压下对实验室模拟电缆缺陷进行局放测试,分别采用所提方法、离散小波变换及自适应双阈值方法进行对比性实验,结果表明,所提方法局放信号漏检率低,去噪效果好。

Partial discharge signals are prone to missed detection under low signal-to-noise ratios,and the traditional singular value decomposition algorithm requires massive calculations when extracting partial discharge pulses.In this regard,a technology of partial discharge pulse extraction and denoising based on random singular value decomposition(RSVD)was proposed.It can extract partial discharge pulses and remove white noises.Compared with traditional SVD pulse extraction and calculation,the method requires a shorter time and has higher engineering practical value.Firstly,the sliding short-time data window was used to intercept the original partial discharge signal fragments.The maximum singular value calculated by RSVD was compared with the global optimal singular value threshold to determine the starting and ending points of pulses.Then the singular value decomposition(SVD)was combined with the local optimal singular value threshold to denoise the extracted signal.Experiments on typical simulated partial discharge pulses verified the superiority of the algorithm’s execution efficiency in pulse extraction.Partial discharge tests were performed on the cable defects simulated in the laboratory at power frequency voltage.Moreover,comparative experiments were conducted for the proposed method,discrete wavelet transform(DWT)plus the adaptive double-threshold method.The results show that the proposed method has a low missed detection rate and a remarkable performance in signal denoising.