基于CEEMDAN算法的光伏功率信号光滑降噪

为解决光伏高输出功率低频分解及光滑降噪问题,通过引入曲线降噪权重因子,改进传统经验模态分解方法,提出一种光滑度与相似度更好的光伏输出功率自适应完备集合经验模态分解算法。为验证CEEMDAN算法的降噪优异特性,对典型光伏输出功率信号开展了模态分解仿真研究,仿真结果表明,相对于与传统EEMD算法,采用CEEMDAN算法时,当IMF为3、目标函数为最小时,相似度提升了3.26%,光滑度提升了85.6%,各项指标数据更加完备,降噪效果更好。     

改进CEEMDAN算法的电机轴承振动信号降噪分析

为提高传统自适应噪声完备经验模态分解算法(CEEMDAN)对电机轴承故障特征信号的精确提取率,降低重构信号失真,提出了一种改进自适应噪声完备经验模态分解算法。首先利用传统CEEMDAN对原始信号初步分解,获得若干特征分量(IMFs)和固有模态分量,将若干IMFs运用熵权法进行初步故障特征信号消噪和提取,对筛选后的IMF分量进行二次分解和二次筛选,获得典型故障敏感信号,再运用SG(Savitzky-Golay)平滑滤波进行信号重构,最终实现电机轴承信号降噪。最后利用凯斯西储大学轴承数据进行改进算法性能分析,结果表明该方法对电机轴承信号能够有效的进行信号降噪,其信噪比相比于原始信号提高2.2 dB。     

基于EEMD算法的10 kV真空断路器分合闸行程信号降噪方法研究

10 kV真空断路器是电网中最关键的电气设备之一,一旦发生故障就将严重威胁电网的安全与稳定运行,因此开展故障诊断方法的研究具有实际工程价值。为克服10 kV真空断路器分合闸动作产生的振动干扰对CCD传感器采集数据的影响,采用EEMD算法对真空断路器分合闸行程信号进行模态分解,并结合IMF频率和能量分析法,对行程信号进行降噪,重构后的行程信号具有很好的降噪效果,可为真空断路器故障提供准确的判断依据。     

基于非线性互补问题函数的半光滑牛顿最优潮流算法

提出了一种新的基于非线性互补问题(NCP)函数的半光滑牛顿方法，以用于求解最优潮流(OPF)问题。通过引入NCP函数，将OPF模型KKT条件的互补松弛约束转化为等式约束，并采用非光滑牛顿法求解。算法的突出优势在于能够有效地处理OPF模型中的不等式约束，从而完全避免了OPF计算中起作用的不等式约束的识别问题。同时，文中利用电力系统的弱耦合特性，构造了牛顿分解算法。IEEE多个算例的数值试验表明：提出的算法具有很好的收敛特性和计算效果，有很好的实际应用前景。     

40CrNiMoA钢疲劳裂纹扩展速率免疫优化计算

基于Paris、Forman及Zheng-Hirt公式,建立了全程疲劳裂纹扩展速率的数学模型,利用免疫算法原理优化出影响汽轮机转子常用钢种40CrNiMoA的疲劳裂纹扩展速率和参数,优化结果符合试验规律,表明采用免疫算法处理多参数非线性疲劳裂纹扩展速率模型效果良好.     

基于自适应集合经验模态分解算法的局部放电信号降噪研究

高压开关柜被广泛应用在电网体系中,保障其运行安全稳定尤为重要.本文针对目前在分析高压开关柜局部放电过程中存在窄带干扰及其他噪声干扰等问题,提出一种新的降噪算法——自适应集合经验模态分解(AEEMD),其核心思想为在原始信号中加入白噪声后只分解一阶模式,然后将噪声不断加到剩余信号中进行循环处理.该方法简化了去噪过程,并减...     

基于小波变换的超声检测信号降噪处理技术

介绍了基于小波变换的超声检测信号降噪处理技术,实际检测信号的降噪处理试验研究表明,应用该技术对超声检测信号进行降噪处理可显著提高信噪比。     

磁悬浮陀螺转子信号的局部均值分解降噪研究

磁悬浮陀螺转子电流信号对环境变化高度敏感,信号采样过程中不可避免会引入噪声,针对该问题提出一种基于局部均值分解（local mean decomposition, LMD）,融合豪斯多夫（Hausdorff）距离与阈值降噪（threshold denoising, TD）的算法以减弱噪声干扰。首先对原始信号进行局部均值分解,得到若干乘积函数（PF）分量和一个余量,然后根据各PF分量与原始信号间的豪斯多夫距离判定噪声、信号分量,再对噪声分量进行阈值处理,最后将阈值处理后的噪声分量、信号分量及余量进行叠加得到重构信号,实现陀螺仪转子电流信号的降噪。仿真实验结果表明,重构信号的信噪比相对于原始信号平均提高了12.86 db,均方根误差平均降低了9.25×10^-6 A;实测信号降噪结果表明,该降噪算法对四条导线边的滤波增益分别为40.0%、93.5%、30.8%和50.0%。     

热工仪表系统的"信号地"电位问题

通过对一些信号地电位处理不当的实例分析,说明在工程设计中地电位问题必须加以重视.     

高压断路器机械振动信号的参数自适应降噪方法

高压断路器动作过程中产生的机械振动信号蕴含着丰富的断路器状态信息,然而,由于受到噪声的影响,有效的状态信息往往难于提取.针对该问题,提出一种谱形态变分模态分解(SSVMD)联合快速奇异值分解(FSVD)的降噪方法.首先,根据振动信号变分模态分解(VMD)结果的频谱特征确定最优分解层数,实现信号各频率成分的自适应分解;然...     

高噪声环境下基于自适应滤波语音降噪技术的研究

为了在高噪声环境下得到较清晰的语音信息,本文提出一种基于线性预测的FIR自适应语音滤波器,对LMS算法作出改进,提高了算法的收敛性。在多种噪声环境下,MATLAB仿真实验结果表明,改进的算法具有更好的降噪效果,显著提高了信噪比,并保持了语音的自然度。     

An adaptive noise reduction filter for discrete signal by use of sandglass‐type neural network

An adaptive noise reduction filter composed of a sandglass‐type neural network (SNN) noise reduction filter (RF) is proposed in this paper. SNN was originally devised to work effectively for information compression. It is a hierarchial network and is symmetrically structured. SNN consists of the same number of units in the input and output layers and a smaller number of units in the hidden layer. It is known that SNN has signal processing performance which is equivalent to Karhunen–Loeve expansion after learning. We proved the theoretical suitability of SNN for an adaptive noise reduction filter for discrete signals. The SNNRF behaves optimally when the number of units in the hidden layer is equal to the rank of the covariance matrix of the signal components included in the input signal. Further we show by applying the recursive least squares method to learning of the SNNRF that the filter can process signals for on‐line adaptive noise reduction. This is an extremely desirable feature for practical application. In order to verify the validity of SNNRF, we performed computer experiments examining how the noise reduction ability of SNNRF is affected by altering the properties of the input pattern, learning algorithm, and SNN. The results confirm that the SNNRF acquired appropriate characteristics for noise reduction from the input signals, and markedly improved the SNR of the signals. © 1999 Scripta Technica, Electr Eng Jpn, 127(4): 39–51, 1999     

水轮机空化声发射信号的联合降噪与特征提取北大核心CSCD

探究水轮机空化诱导的声发射信号随空化状态的变化规律对监测空化具有重要意义。为解决声发射信号携带噪声和特征提取困难的问题,本文建立一种基于自适应迭代滤波分解-奇异谱分析联合降噪和固有时间尺度分解结合特征参数的特征提取方法。首先,采用自适应迭代滤波分解结合相关系数对声发射信号初步降噪,滤除明显噪声分量,重构剩余分量并通过奇异谱分析进一步降噪,将所得信号与趋势分量相加,完成整个降噪过程。然后,对降噪后信号进行固有时间尺度分解,筛选出有效分量,计算其绝对能量和相对能量熵。最后,分析它们随空化系数变化的规律。研究结果表明,绝对能量和相对能量熵随空化系数的变化具有明显的规律性,均能反映水轮机空化的发展状况。     

A novel hybrid fix‐LLL lattice reduction algorithm for MIMO detection system

Lenstra–Lenstra–Lovász (LLL) is an effective lattice reduction algorithm for multiple‐input multiple‐output (MIMO) systems, which was considered to achieve full diversity in the MIMO fading channel. In the LLL algorithm, size reduction is performed for pairs of consecutive basis vectors, and the concern of numerical stability is raised. However, the whole complexity of the LLL algorithm is of polynomial order, and its characteristics of the convergence perform poor in MIMO system. In this paper, a variant version, named the novel hybrid algorithm, which combines both fix measurement and round measurement, is proposed. By modifying the iteration criterion and choosing proper values of the parameters, the algorithm has a large probability to skip the size reduction and cause a faster convergence, It means in one algorithm iteration the LLL potential can be reduced as much as possible. Also, the performance bound derived by the proximity factor shows that the hybrid LLL has a minor performance loss compared to the LLL algorithm. As a direct consequence, in simulation results, the hybrid LLL algorithm can make a better compromise between the rate of convergence, complexity of the algorithm, and algorithmic performance. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于盲信号提取的机械振动信号消噪方法研究

实际环境中,利用传感器测取的机械振动信号中,不可避免的混有来自相邻机械设备及周围环境的噪声干扰,这些噪声对于利用振动信号进行状态检测和故障诊断是很不利的。为了消除机械振动信号中的噪声,本文利用盲信号提取的特征提取特性,提出了一种新颖的噪声消除方法。该方法利用高阶累积量近似信号峭度建立代价函数,通过计算代价函数的梯度不断修正分离矩阵的估计,从而达到分离混合信号的目的。仿真试验在Matlab环境下进行,源信号采用故障轴承缺陷部位所产生的冲击振动信号。分离结果证明该方法能成功提取源信号,有效去除了噪声。     

基于FastICA算法的电力线通信OFDM信号分离方法

提出了一种基于快速独立成分分析(FastICA)算法的电力线通信正交频分复用(OFDM)技术信号分离方法。该方法在FastICA算法对2路观测信号进行解混处理的基础上,将解混信号的前导字段幅度归一化后分别与OFDM符号的前导序列的对应字段进行互相关,取相关值中最大值较大的解混信号作为通信信号,实现了通信信号和噪声的正确识别。定义分离比值为2路相关值中最大值的比值,作为解混效果的评价标准。通过设置合适的分离比值阈值,实现了期望的解混效果。以解混信号的前导字段为参考相位,采用DBPSK调制解调方式,实现了接收数据与解混信号相位无关。建立了MATLAB通信系统仿真模型,仿真结果验证了该方法在低信噪比条件且无纠错编码功能时仍具有较好的通信效果。最后基于硬件平台对算法的可实现性进行了验证测试。     

Indirect current control of active filter for harmonic elimination with novel observer-based noise reduction scheme

Active power filters have been developed recently to solve the harmonics disturbance problems on power system networks. The direct current control scheme is generally used to control the harmonic compensating current directly for current source active power filters. To improve the characteristics of harmonic elimination, the voltage regulation scheme is proposed which regulates the filter capacitor voltage at the input side. Hence the voltage regulation scheme is an indirect current control technique for making sinusoidal line current. To implement this proposed scheme, the electronic differentiator should be used. By using the conventional differentiator, high frequency noise is generated in the controller. This deteriorates both the accuracy and reliability of the controller. To reduce this noise, a novel observer-based noise reduction scheme is proposed for our active filter system. The observer-based algorithm and characteristics of indirect current-controlled active filters are investigated through simulations and experiments.     

测量噪声方差未知的多传感器组合导航集中融合算法

目前,多传感器组合导航系统的信息融合方法是建立在测量噪声方差已知的基础上,然而测量噪声方差会随着内部及外部的干扰而发生变化。为此,本文首先将基于变分贝叶斯逼近的自适应卡尔曼滤波(variational Bayesian approximation based adaptive Kalman filter, VB-AKF)从单一组合导航系统扩展到多传感器组合导航系统;然后,提出了多传感器组合导航系统的两种集中融合算法,即基于VB-AKF的增广式集中融合算法及基于VB-AKF的序贯式集中融合算法,以解决测量噪声方差未知情况下的多传感器组合导航的信息融合问题;最后,通过SINS/GNSS/CNS/ADS多传感器组合导航系统对上述算法进行了仿真验证。实验结果表明,本文所提两种算法滤波精度相同、且接近于测量噪声方差已知情况下的理想集中融合算法(ICKF)。在整个仿真时段内,相对于传统集中式卡尔曼滤波器(TCKF)及具有容错功能的联邦卡尔曼滤波算法(FT-FKF),本文算法可提高位置精度分别为32%和90%、提高速度精度分别为38%和71%。