基于独立分量分析的电力系统瞬时电压畸变信号判别方法

提出一种基于独立分量分析(independent component analysis,ICA)的瞬时电能质量扰动信号检测与判别方法。利用ICA可将相互独立的源信号从其线性混合信号中分离出来的特点,以负熵作为衡量信号独立性的目标函数,通过优化此函数,得到一种固定点算法：FastICA算法,用此算法对包含瞬时电能质量扰动信号的电网电压信号进行计算,可分离出与扰动相对应的信号。对于不同类型的扰动,分离出的信号具有不同的波形特征,根据这一特点,可对扰动进行判别并确定其位置和持续时间。仿真试验结果表明,该方法对瞬时电压跌落、瞬时电压上升、瞬时脉冲、瞬时电压中断、谐波等多种瞬时电压畸变信号有较好的检测与判别效果。     

High‐speed blind speech separation using FICS method

In this paper, we introduce the fusion of iterative and closed‐form separation (FICS) method for high‐speed separation of mixed speech signals. This method is performed in two stages: (i) iterative‐form separation and (ii) closed‐form separation. This algorithm significantly improves the separation quality simply by incorporating only some specific frequency bins into computations. We apply the FICS method to the frequency‐domain independent component analysis (ICA) to evaluate its performance in increasing the signal separation speed. Simulation results show that for speech signals, the proposed algorithm is on average 40 times faster than the ICA, while preserving the separation quality. Also, it outperforms the FastICA, JADE, and SOBI in terms of separation quality. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于JADE算法的变压器振动信号分离的研究

为了实现变压器绕组和铁心振动信号的分离,从而达到对绕组与铁心运行状态监测的目的,文章利用独立分量分析理论(ICA)与联合近似对角化算法(JADE),将变压器铁心、绕组的振动信号从传感器监测到的混合信号中分离出来,并根据各个部件振动信号与数据库原始信号中的频率特性对比分析,判断变压器的故障隐患。采用LabVIEW与MATLAB混合编程技术对JADE算法进行编程,由仿真结果可知:在信号源和混合参数未知的情况下,JADE算法能根据观测信号以及源信号统计独立的假设对源信号进行可靠分离。     

独立分量分析在输电塔防盗监测系统中的应用

在电磁干扰下检测出因不良企图引起的铁塔振动信号,防止塔材被盗事件的发生,对保障电网安全运行有着重要意义。文中介绍了一种基于独立分量分析（ICA）的输电塔防盗监测系统。ICA方法能够在没有源信号和传输通道参数先验知识的情况下,按照统计独立的原则,通过选择判据和优化算法将信号分解成若干独立的源成分,适合于该系统中非平稳振动信号的提取。系统在硬件方面采取了相应的抗干扰措施,有效降低了电磁等干扰。采用负熵的FastICA方法和探查性投影追踪信号处理算法快速地对铁塔盗窃中产生的振动信号进行了较好的提取和分离,结合自适应阈值脉冲提取算法,有效地提取振动脉冲成分,降低了噪声干扰。实验结果表明该系统能够较好地检测出铁塔盗窃中产生的振动信号。     

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     

A fast second‐order signal separation algorithms with on‐line capabilities

In correlation‐based signal separation algorithms, the received mixed signals are fed to a de‐coupling system designed to minimize the output cross‐correlation functions. If minimizaion is perfect, each of the system's outputs carries only one signal independent of the others. In these algorithms, the computation burden of the output cross‐correlation functions normally slows down the separation algorithm. This paper, describes a computationally efficient method for off‐line pre‐computation of the needed cross‐correlation functions. Explicit formulas have been derived for the output cross‐correlation functions in terms of the received input signals and the de‐coupling system parameters. Then, it is shown that signal separation amounts to the least‐squares solution of a system of linear equations describing these output cross‐correlation functions, evaluated over a batch of lags. Next, a fast RLS‐type adaptive algorithm is devised for on‐line signal separation. In this respect, an algorithm is derived for updating the de‐coupling parameters as data comes in. This update is achieved recursively, along the negative of the steepest descent directions of an objective cost function describing the output cross‐correlation functions over a batch of lags, subject to equal output power constraints. Illustrative examples are given to demonstrate the effectiveness of the proposed algorithms. Copyright © 2002 John Wiley & Sons, Ltd.     

基于小波分量奇异值分解的单通道盲分离算法

针对单通道盲分离领域先验信息不足的问题,提出了一种基于小波变换、奇异值分解和独立分量分析的单通道盲分离算法.首先对单路传感器接收的信号进行小波分解和重构,得到一组代表原始信号特征的细节信号,对其施加奇异值分解并剔除小于门限值的奇异点,以此消除干扰信号和噪声的影响.然后将经过处理的细节信号组作为独立分量分析算法的输入信号...     

一种基于ICA的图像信息隐藏算法

独立分量分析（ICA）是一种基于高阶统计量的信号分析方法,它可以找到隐含在数据中的独立分量,已广泛应用于信号处理领域。信息隐藏是一种新兴的技术,其目的在于将秘密信息隐藏于另一非机密信息中。本文提出一种新的信息隐藏技术,即将Arnold置乱后的秘密图像嵌入到载体图像中,再利用ICA算法从中提取出秘密图像。仿真结果表明该算法有效可行。     

基于改进DEMD和ICA的海上风机传动系统早期故障诊断

针对传动系统早期故障振动信号较弱的情况,提出基于改进微分经验模式分解(DEMD)和独立分量分析(ICA)的海上风机传动系统早期故障诊断方法。为克服传统的DEMD算法在分解低阶本征模态函数(IMF)时存在失真现象,提出改进的微分经验模式算法将原始振动信号分解成若干个独立的IMF信号,结合ICA进一步进行原始振动信号故障特征分量的提取,并基于标准数据和风机动力传动故障诊断实验平台进行了仿真研究,最后选取海上风电机组传动系统常出现的发电机轴承故障进行诊断分析。结果表明,相对于传统的故障诊断方法,该方法能更好地放大故障分量,减少噪声和其他振动干扰信号的影响,提高了海上风电机组传动系统早期故障诊断的准确性。     

An accurate forearm EMG signal classification method using two‐channel electrode

An accurate electromyography (EMG) classification algorithm to control a virtual hand prosthesis with 12 degrees of freedom using two surface EMG electrodes is presented in this paper. We propose the application of independent component analysis (ICA) for blind‐source separation of the EMG signals obtained from two electrodes. One of the problems affecting the EMG classification accuracy is the location dependence of the EMG signal due to the superposition of signals from multiple sources. ICA is used to separate the two signals obtained from two surface electrodes into two independent EMG signals prior to the feature extraction and classification processes. We demonstrate that the EMG classification accuracy can be improved using the ICA algorithm. We also propose a novel eigen‐based feature that is extracted from the short‐time Fourier transform (STFT) magnitude spectrum. Our new feature not only decreases feature dimensions but also performs better than other well‐known features. We also implement the EMG classification scheme on the virtual robot arm. The performance shows promising result as indicated by a decrease in the Davies–Bolden (DB) index after applying the ICA © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于EMD的ICA方法在电力载波通信信号提取中的应用

提出了1种结合经验模态分解的独立分量分析方法：首先对采样得到的1路电网电压信号进行经验模态分解，得到1组固有模态函数；通过计算它们的互相关系数，找出独立性最强的几个，接着用独立分量分析的方法处理这几个固有模态函数与原采样到的电压信号，最后得到1组彼此独立的独立分量，从而提取出通信信号。该方法综合了经验模态分解和独立分量分析的各自的优点，克服了独立分量分析不能对单一采样信号进行分析的缺点，利用独立分量分析方法提高了信号提取的效果。实验表明：文中提出的方法是有效且可行的。     

独立成分分析方法在电站热力过程数据检验中的应用

提出了基于独立成分分析的电站热力过程数据检验方法,通过对测量数据的特征提取,对原测量数据中的信号进行分离,再通过独立成分分析进行信号复原,实现测量数据的重构。对独立成分分析算法、成分数目选择和初始化、检验阈值进行了讨论,给出了不良数据检验的流程,该方法计算量不大,可以用于在线数据检测。选用300 MW机组热力系统21个实际测量参数作为算例,表明提出的数据检验方法能有效地检测到给水温度测量数据中的不良值,并可以给出可信的重构数据。对独立成分的信号分离能力进一步分析验证,表明了独立成分对测量数据中信息的表达能力。     

Blind MIMO equalization with optimum delay using independent component analysis

Blind space–time equalization of multiuser time‐dispersive digital communication channels consists of recovering the users' simultaneously transmitted data free from the interference caused by each other and the propagation effects, without using training sequences. In scenarios composed of mutually independent non‐Gaussian i.i.d. users' signals, independent component analysis (ICA) techniques based on higher‐order statistics can be employed to refine the performance of conventional linear detectors, as recently shown in a code division multiple access environment (Signal Process 2002; 82 :417–431). This paper extends these results to the more general multi‐input multi‐output (MIMO) channel model, with the minimum mean square error (MMSE) as conventional equalization criterion. The time diversity introduced by the wideband multipath channel enables a reduction of the computational complexity of the ICA post‐processing stage while further improving performance. In addition, the ICA‐based detector can be tuned to extract each user's signal at the delay which provides the best MMSE. Experiments in a variety of simulation conditions demonstrate the benefits of ICA‐assisted MIMO equalization. Copyright © 2004 John Wiley & Sons, Ltd.     

一种基于ICA和DWT的数字图像水印算法

数字水印是图像处理领域研究的一个热点。独立分量分析（ICA）是一种基于高阶统计量的信号分析方法,它可以找到隐含在数据中的独立分量,已广泛应用于信号处理领域。提出了一种结合独立分量分析（ICA）和离散小波变换（DWT）的数字图像水印方法。该算法将原始图像进行小波分解,然后在小波逼近子图嵌入置乱后的水印图像。数字水印的检测使用了快速独立分量分析（FastICA）的方法,最后对分离出的水印进行增强处理。仿真结果表明该算法有效可行,具有较强的鲁棒性和隐蔽性。     

基于小波包与改进的PSO-PNN变压器励磁涌流识别算法研究

利用小波包对励磁涌流和故障电流信号进行分解并提取小波包能量特征。采用改进粒子群(PSO)算法训练概率神经网络(PNN)寻找全局最优,对PNN网络的输入输出、传递函数以及隐含层节点数进行确定,建立PNN的网络模型,对网络进行训练测试,最后提出保护判据。研究发现,该算法不仅训练速度和收敛速度快,而且具有较高的识别精度。     

A novel method for power quality multiple disturbance decomposition based on Independent Component Analysis

In this paper, a novel method for power quality signal decomposition is proposed based on Independent Component Analysis (ICA). This method aims to decompose the power system signal (voltage or current) into components that can provide more specific information about the different disturbances which are occurring simultaneously during a multiple disturbance situation. The ICA is originally a multichannel technique. However, the method proposes its use to blindly separate out disturbances existing in a single measured signal (single channel). Therefore, a preprocessing step for the ICA is proposed using a filter bank. The proposed method was applied to synthetic data, simulated data, as well as actual power system signals, showing a very good performance. A comparison with the decomposition provided by the Discrete Wavelet Transform shows that the proposed method presented better decoupling for the analyzed data.     

采用受扰轨迹和独立分量分析技术识别同调机群的方法

针对大区电网互联运行中出现振荡失步时,需要正确划分同调机群以解列电网的问题,提出采用独立分量分析技术(independent component analysis,ICA)对故障后的发电机受扰轨迹进行特征提取,来识别系统中同调机群的方法。与现有方法相比,这种方法不需要获得系统元件模型和参数,而是直接球化广域测量系统(wide area measurement systems,WAMS)提供的发电机电角速度信号,再利用最大负熵准则进行独立分量分析得到特征矩阵,将高维数的多机受扰轨迹数据变换到低维空间,通过模式识别得到分群结果。8机36节点系统和西北750 kV电网规划系统算例表明,该方法能有效消除噪音和坏数据的影响,准确识别出同调机群。     

一种改进的主汽温过程模型神经网络辨识方法

介绍了一种用RBF网络进行主汽温系统辨识的一种方法，采用串一并联型的辨识结构，其输入为待辨识对象的输入和输出，导师信号为待辨识对象的输出，隐层的结构函数采用高斯函数，训练时的输入信号采用阶跃信号与白噪声信号的叠加信号，仿真结果表明，这种方法对系统有非常好的辨识能力。     

基于CEEMDAN-ZCR的光纤电流互感器非线性误差识别

针对光纤电流互感器(FOCT)漂移、变比波动等非线性误差问题,提出了一种基于自适应噪声完备集合经验模态分解(CEEMDAN)-过零率(ZCR)的光纤电流互感器误差识别算法。首先,利用CEEMDAN算法对光纤电流互感器输出电流信号进行分解,得到包含非线性误差特征的固有模态分量(IMF),构成原始误差向量数据集。然后,对比不同误差下的分量数量,利用ZCR算法计算不同误差下各个IMF分量的过零率指标,用于将IMF分类。最后,根据ZCR指标呈现出的特点,将IMF分量信号分为三类,并叠加重组为三个分量,构建出分解结果数量稳定的IMF分量信号,根据不同分量的特征实现误差识别。结果表明：基于CEEMDAN-ZCR的误差识别算法能够有效的识别两种误差,其中漂移误差特征主要集中在IMF中第三层,变比误差主要集中在IMF中第二层,验证了本方法的有效性。     

基于EMD盲源分离算法的电力系统低频振荡模式识别

针对广预测量系统低频振荡过程中的高斯噪声干扰和定阶问题,提出了基于EMD(empirical mode decomposition)盲源分离(blind source separation,BSS)算法的单通道低频振荡信号的模式分析方法。首先将信号利用经验模态分解得到一系列本征模函数分量组合的新信号;其次针对存在模态混叠的本征模函数分量,提出利用信号周期性构造其多路信号,并利用独立分量分析消除模态混叠的有效方法;然后利用盲源分离技术--二阶盲辨识算法(second order blind identification,SOBI),处理多通道观测信号矩阵,从中提取出不同的单模式信号;最后将去噪、定阶后的信号运用最小二乘-旋转不变技术(TLS-ESPRIT)算法辨识,得到低频振荡模态参数。数值算例仿真、IEEE四机两区域仿真实验表明该算法能够有效分离源信号,相比于其他方法具有抗噪性能好、拟合精度高等优点。