基于图像信息熵与多元变分模态分解的电缆局放信号去噪方法

A Denoising Algorithm for Cable Partial Discharge Signals Based on Image Information Entropy and Multivariate Variational Mode Decomposition

局部放电(PD)检测是评估交联聚乙烯(XLPE)电缆绝缘状态的主要手段。针对电缆终端与中间接头局部放电现场检测时存在白噪声、周期性窄带干扰,以及去噪过程中自适应性较弱的问题,该文提出了一种基于图像信息熵与新型自适应多元变分模态分解的去噪方法。首先,对信号进行多元变分模态分解,重组信号并转换成灰度图像,进而计算图像一维信息熵。在考虑算法执行效率的同时,将Pearson相关系数与图像信息熵优化算法的模态参数相结合。其次,通过计算各本征模态分量的峭度来判定其主导分量的性质特征,利用峭度对噪声敏感的特性区分PD特征信息与噪声干扰分量,进而对噪声干扰分量进行3σ准则滤波。最后,通过新型改进小波阈值算法得到去噪信号。利用该方法对PD信号进行去噪,并与基于Spearman的变分模态分解(S-VMD)法、自适应集合经验模态分解(NAEEMD)法、短时傅里叶变换-奇异值分解(STFTSVD)法进行对比分析。结果表明,该方法对现场PD信号具有良好的抑噪性能,且耗时少、执行效率高、工程应用价值高。

In recent years,cross-linked polyethylene cables have been developed in transmission lines and urban distribution networks due to their advantages,lightweight,high-temperature resistance,and high transmission power.With the increase in the number of XLPE power cables put into operation and the extension of cable lines,silicone rubber insulated prefabricated intermediate joints are widely used in XLPE power cables due to their excellent high-voltage shielding performance,overall prefabricated design,reliable grounding,large voltage margin,and convenient on-site installation.The intermediate cable joint is usually installed and formed on the laying site,which can quickly leave a hidden danger of cable operation failure.Because it comprises multi-layer solid composite structures with different dielectric properties,the probability of accidents is much higher than that of the cable body.Partial discharge(PD)detection is the main means of evaluating the insulation status of XLPE cables and the manufacturing and installation defects of cables.A PD denoising method based on image information entropy and novel adaptive multivariate variational mode decomposition(MVMD)is proposed to address the issues of white noise,periodic narrowband interference,and poor adaptability in on-site detection of PD at cable terminals and intermediate joints.Firstly,optimize the parameters of the MVMD algorithm by integrating multiple factors,and then,based on parameter optimization,perform modal decomposition on the noisy PD signal.Secondly,the kurtosis of each eigenmode component is calculated,and the kurtosis of the sine signal and double exponential decay signal at the signal-to-noise ratio of 0dB is calculated by using the characteristic that kurtosis is sensitive to noise to distinguish the PD characteristic information from the noise interference component.Then,the 3σcriterion is used to filter white noise with normal distribution.Finally,based on the improved new wavelet threshold function,the reconstructed PD signal is denoised to obtain the denoised PD signal.The following conclusions can be drawn by comparing the method with other denoising algorithms:(1)The Spearman variational mode decomposition(S-VMD)can improve modal aliasing,but there is still residual noise in the denoised signal.Hence,the denoising effect is not ideal.(2)The novel adaptive ensemble empirical mode decomposition(NAEEMD)cannot wholly eliminate modal aliasing,resulting in a certain degree of displacement of the discharge starting position and affecting subsequent diagnosis and positioning.(3)Although the short-time Fourier transform and matrix factorization(STFT-SVD)can effectively suppress white noise and periodic narrowband interference,the denoised PD signal contains residual noise,and the execution efficiency of this algorithm is low.(4)By calculating various evaluation indicators,the method has a good denoising effect on the on-site noisy PD signal.At the same time,this method has the advantages of less time consumption and high execution efficiency.The following conclusions can be drawn:(1)The information entropy is used to measure the aggregation characteristics of the gray image distribution accurately and then to determine the certainty of the PD pulse signal.velocity of 3.0 m/s,which corresponds to a maximum critical contact gap for the formation of footpoint mode and anode spot mode.In this case,the high current vacuum arc could quickly transit from the intense arc mode into diffuse arc mode in short arcing time high current interruption.Meanwhile,the formation of anode spot could be effectively avoided in long arcing time high current interruption.In addition,the transition of anode discharging mode for the test AMF contact could be predicted by comparing the actual axial magnetic flux density,Baxial,with the critical quantities,Bcri_intense and Bcri_anode.These critical axial magnetic flux densities were experimentally determined for the transitions of intense arc mode to diffuse arc mode,and diffuse arc mode to footpoint mode and anode spot mode,respectively.The above v1 and v2 were applied on a 126 kV single-break VCB,and proved to be effectiveness by passing series of short-circuit current breaking type test.The result could provide reference and foundation for VCBs developing towards transmission voltage level.