Research on the longitudinal protection of a through-type cophase traction direct power supply system based on the empirical wavelet transform

This paper proposes a longitudinal protection scheme utilizing empirical wavelet transform(EWT)for a through-type cophase traction direct power supply system,where both sides of a traction network line exhibit a distinctive boundary structure.This approach capitalizes on the boundary’s capacity to attenuate the high-frequency component of fault signals,resulting in a variation in the high-frequency transient energy ratio when faults occur inside or outside the line.During internal line faults,the high-frequency transient energy at the checkpoints located at both ends surpasses that of its neighboring lines.Conversely,for faults external to the line,the energy is lower compared to adjacent lines.EWT is employed to decompose the collected fault current signals,allowing access to the high-frequency transient energy.The longitudinal protection for the traction network line is established based on disparities between both ends of the traction network line and the high-frequency transient energy on either side of the boundary.Moreover,simulation verification through experimental results demonstrates the effectiveness of the proposed protection scheme across various initial fault angles,distances to faults,and fault transition resistances.

Variational Mode Decomposition-Informed Empirical Wavelet Transform for Electric Vibrator Noise Analysis

Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) offer valuable support for studying signal components, they also present certain limitations. This article integrates the strengths of both methods and proposes an enhanced approach that integrates VMD into the frequency band division principle of EWT. Initially, the method decomposes the signal using VMD, determining the mode count based on residuals, and subsequently employs EWT decomposition based on this information. This addresses mode aliasing issues in the original method while capitalizing on VMD’s adaptability. Feasibility was confirmed through simulation signals and ultimately applied to noise signals from vibrators. Experimental results demonstrate that the improved method not only resolves EWT frequency band division challenges but also effectively decomposes signal components compared to the VMD method.

基于EWT-KLD的机械密封金刚石涂层磨损声发射降噪

为了准确获得机械密封金刚石涂层在磨损过程的声发射信号,在分析机械密封设备的噪声特性基础上,提出了基于经验小波变换(EWT)和相对熵(KLD)的声发射降噪方法;通过对磨损声发射信号进行经验小波变换得到划分其频带的滤波器组,对磨损声发射信号和背景噪声发射信号用相同的滤波器组划分频带;计算相应频带2种信号的相对熵,用累计和算法在升序排列的相对熵中找到首个大于3σ的值作为阈值,保留相对熵值大于阈值的频带重构信号,完成降噪.研究结果表明:本文所提的EWT-KLD方法可以有效抑制不同工况、不同磨损状态的声发射信号的噪声,有效改善了磨损声发射信号的信噪比,尤其是微弱磨损信号的信噪比,提高了密封端面磨损声发射检测的精度和灵敏度;通过与传统降噪方法的对比发现,本文方法能够对不同工况下的密封磨损声发射信号降噪表现出更强的适应性和稳定性,对于及时检测早期密封磨损和准确监测磨损累积变化过程具有重要意义.

基于声振融合的二次EWT-CNN刀具磨损监测

为了实现加工过程中对刀具磨损状态的监测,提出一种基于协同过滤融合的方法。首先,对工作刀具振动信号和声音信号进行特征相关性分析后进行数据层融合;然后,将得到的声振融合信号进行二次经验小波变换(EWT)后去噪重构;最后,将重构信号进行信号增强并送入CNN实现特征提取及刀具故障识别。通过对不同故障类型的麻花钻头进行故障识别实验,在声音、振动以及声振融合信号和不同信号去噪重构方法的对比下,该方法对不同故障类型的钻头作出了98.96%的高识别率。验证了所提方法在刀具故障识别方面的优越性。

A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy

Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields.Reverse dispersion entropy(RDE)proposed by us recently,as a nonlinear dynamic analysis method,has the advantages of fast computing speed and strong anti-noise ability,which is more suitable for measuring the complexity of signal than traditional permutation entropy(PE)and dispersion entropy(DE).Empirical wavelet transform(EWT),based on the theory of wavelet analysis,can decompose a complex non-stationary signal into a number of empirical wavelet functions(EWFs)with compact support set spectrum,which has better decomposition performance than empirical mode decomposition(EMD)and its improved algorithms.Considering the advantages of RDE and EWT,on the one hand,we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy;on the other hand,we use RDE as the features of EWFs to improve the signal separability and stability.Finally,we propose a novel signal feature extraction technology based on EWT and RDE in this paper.Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals.Moreover,it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.

基于EWT的分布式光储PCC功率波动自适应平抑方法

分布式光伏在交流侧公共连接点(point of common coupling,PCC)汇流的功率有较大的随机性与波动性,影响电网的稳定运行。为此,提出了基于经验小波变换(empirical wavelet transform,EWT)的分布式光储PCC功率自适应平抑方法。首先,针对混合储能(hybrid energy storage system,HESS)与分布式光伏接入PCC的典型场景,在分析EWT自适应处理波形的特点后,结合功率波动率与储能元件的响应特性,对PCC的光伏原始汇流功率进行EWT分解与优化修正,实现HESS的功率初级分配。之后为避免HESS的荷电状态(state of charge,SOC)频繁越限,提出了一种主动功率补偿的SOC控制策略,通过主动改变储能的参考信号使其SOC在安全范围内工作。结合实际数据的仿真验证表明,该平抑方法能够自适应地实现光伏出力的合理分解与功率分配,在延长储能使用寿命的同时有效满足并网功率波动的要求,为平抑光伏输出功率波动提供了新思路。

基于EEWT的电力系统超高次谐波检测

随着电力系统逐渐电力电子化,导致大量超高次谐波注入电网,引发了新的电能质量问题。为了解决这一问题,首要任务是对其进行精准的测量。此处提出了一种基于改进经验小波变换(EEWT)的超高次谐波检测方法。根据超高次谐波信号的频域特点,对经验小波变换中原有的频带边界划分方法进行了改进,并通过计算过零率和相关系数对信号中的噪声进行有效滤除。再利用经验小波变换技术,实现各超高次谐波信号的分离,从而对幅值和频率的参数进行检测。通过仿真与实验对实际高频光伏逆变器所产生的超高次谐波进行检测。结果表明了理论分析的准确性以及参数检测结果的精度,从而最终验证了所提方法在实际应用中的可行性以及有效性。

基于EWT-FE分析联合改进SVM算法的GIS局部放电诊断方法

为提高气体绝缘组合电器(Gas insulated switchgear,GIS)局部放电类型诊断的精度,提出了一种基于EWT-FE结合IHPO-SVM算法的GIS局部放电诊断方法。为深度挖掘局部放电信号内部特征,利用经验小波变换(Empirical wavelet transform,EWT)结合模糊熵(Fuzzy entropy,FE)算法对信号进行分解,并提取有效特征量;为提高支持向量机(Support vector machine,SVM)算法自适应能力与分类识别精度,提出利用经过余弦衰减计算方法以及指数下降函数改进的猎人猎物优化(Improved hunter-prey optimizer,IHPO)算法对SVM算法参数进行优化选取;搭建GIS局部放电试验模型,建立基于EWT-FE信号分析结合IHPO-SVM的局部放电识别模型,对所提算法有效性进行验证。试验结果表明,所提算法GIS局部放电类型诊断精度均大于95%,优于传统诊断算法。

Empirical Wavelet Transform;Stationary and Nonstationary Signals

Signal decomposition into the frequency components is one of the oldest challenges in the digital signal processing.In early nineteenth century,Fourier transform(FT)showed that any applicable signal can be decomposed by unlimited sinusoids.However,the relationship between time and frequency is lost under using FT.According to many researches for appropriate time-frequency representation,in early twentieth century,wavelet transform(WT)was proposed.WT is a well-known method which developed in order to decompose a signal into frequency components.In contrast with original WT which is not adaptive according to the input signal,empirical wavelet transform(EWT)was proposed.In this paper,the performance of discrete WT(DWT)and EWT in terms of signal decomposing into basic components are compared.For this purpose,a stationary signal including five sinusoids and ECG as biomedical and nonstationary signal are used.Due to being non-adaptive,DWT may remove signal components but EWT because of being adaptive is appropriate.EWT can also extract the baseline of ECG signal easier than DWT.

Price prediction of power transformer materials based on CEEMD and GRU

The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.

Fault DiagnosisMethod of Energy Storage Unit of Circuit Breakers Based on EWT-ISSA-BP

Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers(LVCBs).A fault diagnosis algorithm based on an improved Sparrow Search Algorithm(ISSA)optimized Backpropagation Neural Network(BPNN)is proposed to improve the operational safety of LVCB.Taking the 1.5kV/4000A/75kA LVCB as an example.According to the current operating characteristics of the energy storage motor,fault characteristics are extracted based on Empirical Wavelet Transform(EWT).Traditional BPNN has problems such as difficulty adjusting network weights and thresholds,being sensitive to initial weights,and quickly falling into local optimal solutions.The Sparrow Search Algorithm(SSA)with self-adjusting weight factors combined with bidirectional mutations is added to optimize the selection of BPNN hyperparameters.The results show that the ISSA-BPNN can accurately and quickly distinguish six conditions of motor voltage reduction:motor voltage increase,motor voltage decrease,energy storage spring stuck,transmission gear stuck,regular state and energy storage spring not locked.It is suitable for fault diagnosis and detection of the energy storage part of LVCB.

基于EWT-SVM的雨量识别方法

为了从雨声信号中识别出雨量的大小,提出了一种基于经验小波变换和支持向量机的雨量识别算法。对于采集到的雨声信号先进行去噪,接着对信号进行经验小波变换分解,分解后得到数个经验小波函数分量,然后通过Matlab编程对各个经验小波函数分量进行特征提取,在时域和频域范围内组成评价特征矩阵,最后通过SVM对特征矩阵进行分类识别。通过仿真实验发现,对于同一个信号,经验小波函数相较于经验模态分解有更好的自适应性并且克服了经验模态分解的混叠现象和端点效应。实验结果表明基于经验小波变换和支持向量机的雨量识别方法在雨量识别领域具有良好的效果,研究方法为雨量识别、智能雨量计的发展奠定了良好的基础。

基于EWT-CNN-BiGRU的多特征电力负荷预测模型

针对目前多特征预测模型在短期电力负荷数据上精度不足的问题,提出一种基于经验小波变换(EWT)的卷积神经网络(CNN)融合双向门控循环单元(Bi GRU)预测模型。首先,从多维时序数据中提取强关联性特征,其次,对选定特征进行经验小波变换,将时序数据映射至频域以获取子序列,最后,通过卷积神经网络和双向门控循环单元融合模型实现对电力负荷数据的预测。该预测模型使用德国某联合循环电厂的时序数据进行实验验证。结果表明,该预测模型获得99.463%的拟合优度,具有较好的预测效果。

基于IEWT-MOMEDA-FSC的滚动轴承故障诊断

针对滚动轴承故障信号常伴有噪声干扰且故障特征难以提取的问题,本文提出一种基于改进经验小波变换(IEWT)、多点优化最小熵解卷积(MOMEDA)和快速谱相关(FSC)的滚动轴承故障诊断方法.首先,将原始信号进行快速谱相关分析得到增强包络谱,通过增强包络谱的极值点来自适应地划分频谱,以分割的频谱为边界构建小波滤波器组将信号分解为多个IMF分量,利用相关峭度准则筛选出有效的分量进行叠加;其次,用MOMEDA对其进行降噪处理,将降噪后的信号进行快速谱相关分析,得到增强包络谱图;最后,将增强包络谱图中幅值较高的频率与故障频率对比,判定其失效形式,用所提出的方法对实测轴承故障信号进行分析验证.结果表明,所提出的方法能有效降低噪音干扰且增强信号故障冲击特性,在噪声环境下具有较强的故障特征提取能力.

基于SS-EWT的IGBT驱动电路早期故障诊断

驱动电路是绝缘栅双极性晶体管(Insulated Gate Bipolar Transistor,IGBT)正常运行的重要保障。本文基于模拟电路早期故障理论,针对以EXB841驱动模块为核心的300 A/1200 V快速型IGBT驱动电路,提出了一种基于尺度空间(Scale Space,SS)改进的经验小波变换(Empirical Wavelet Transform,EWT)和误差反向传播神经网络(Back Propagation Neural Network,BPNN)相结合的早期故障诊断方法。首先在分析驱动电路硬件结构及工作原理的基础上,对其驱动功能和短路保护功能的主要元件进行灵敏度分析,分别选取相应的早期故障诊断元件;其次通过故障注入获取检测点输出数据,采用SS-EWT对数据处理后构造早期故障特征向量;最后采用BPNN对特征向量进行训练以实现对早期故障模式的识别。针对短路保护元件部分故障类别难以区分的问题,采用再训练对诊断方法进行优化。仿真结果表明,采用基于SS-EWT的早期故障诊断方法处理驱动功能元件及短路保护元件早期故障时准确率分别能达到91.04%和96.05%。

基于S-EWT的带式输送机声音信号去噪算法研究

针对复杂工作环境下带式输送机有效声音信号提取难的问题,拟提出一种改进经验小波变换算法,利用插值法对傅里叶频谱重建,计算各个频段的局部功率,设置阈值优化分割频段,根据分解的调幅调频信号重构声音信号,提取声音信号特征参数,输入支持向量机进行故障分类验证算法可行性。通过对港口采集的带式输送机声音信号进行仿真实验,结果表明,改进算法在克服原有缺陷的同时,去噪能力上提升了25%左右,故障诊断平均准确率达到96%。

基于EWT和NeuralProphet-MLP的蜂窝网络流量长期预测方法

蜂窝网络流量长期预测对网络扩展和优化具有重要意义,针对长期预测中数据可用性低以及非线性等弊端所带来的诸多挑战,提出一种基于分解的分频预测模型。分别采用NeuralProphet模型和多层感知机对分解出的低频分量和中高频分量进行预测,最后对各分量预测结果进行逆经验小波变换得到最终结果。在真实的蜂窝网络流量数据集上进行验证,结果表明所提方法相较于传统预测模型在准确度上有较大提升,具有较好的应用价值。

改进小波阈值函数和ACEWT方法的滚动轴承故障声发射信号特征提取

针对滚动轴承早期故障声发射信号受复杂传递路径和噪声的干扰,声发射信号信噪比较低,导致轴承故障特征难以提取的问题,提出了改进小波阈值函数-ACEWT的轴承故障特征提取方法。由于声发射信号呈冲击性与快速衰减的特点,构建一种衰减正弦型与指数型的小波阈值函数对低信噪比的声发射信号进行降噪。研究自相关运算与经验小波变换结合的方法(autocorrelation and empirical wavelet transform,ACEWT),用于滚动轴承故障声发射信号特征提取,解决了在低信噪比下经验小波变换对轴承故障特征提取的不足;引入经验小波能量比-熵指标,选取最优经验小波系数。通过与经验小波变换、改进小波阈值函数-EWT和MCKD-EWT方法进行对比研究,并试验验证。仿真和试验结果表明,所提方法明显优于经验小波变换、改进小波阈值函数-EWT和MCKD-EWT方法,可准确提取轴承故障声发射信号的频率特征。

Empirical Wavelet Transform Based Method for Identification and Analysis of Sub-synchronous Oscillation Modes Using PMU Data

This paper proposes an empirical wavelet transform(EWT)based method for identification and analysis of sub-synchronous oscillation(SSO)modes in the power system using phasor measurement unit(PMU)data.The phasors from PMUs are preprocessed to check for the presence of oscillations.If the presence is established,the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida algorithm.The superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest China.Results show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.

基于AR-EWT和SSI的桥梁结构模态参数识别

为解决随机子空间识别(SSI)算法存在的系统定阶困难问题,避免各模态之间的相互影响,提出一种结合自回归功率谱经验小波变换(AR-EWT)和SSI的桥梁结构模态参数识别方法。首先,采用AR-EWT将结构不同测点的振动响应信号分解成一系列仅含单一模态信息的单分量信号,并利用奇异值分解(SVD)去噪单分量信号。然后,组装所有测点中包含相同模态信息的单分量信号。最后,利用SSI分别处理各组单分量信号,识别结构的自振频率、阻尼比和振型。ASCE Benchmark模型、简支梁模型和西宁北川河桥的模态参数识别结果表明:所提出的AR-EWT结合SSI的模态参数识别方法可避免直接利用SSI识别模态参数时存在的系统定阶问题,提高模态参数识别效率;ASCE Benchmark模型、简支梁模型和西宁北川河桥的模态参数识别结果与直接利用SSI的识别结果、有限元分析结果基本一致。