一种模糊变步长自适应谐波检测算法

电力有源滤波器的成功应用依赖于精确的谐波电流检测技术。基于自适应干扰对消理论,提出一种基于模糊变步长推理的最小均方差（LMS）自适应谐波检测算法。通过分析影响LMS自适应谐波算法性能的不利因素,选取均方误差变化量和输入输出信号相关函数作为参量,建立模糊推理系统,自适应地调节算法的步长,实现谐波检测过程中,既能保证较快的动态响应速度和对噪声干扰的抑制,又能保持较高的检测精度,并通过计算机仿真及物理实验验证了该算法的有效性和可行性。     

Fast frequency and harmonic estimation in power systems using a new optimized adaptive filter

This paper presents an adaptive filter for fast estimation of frequency and harmonic components of a power system voltage or current signal corrupted by noise with low signal to noise ratio (SNR). Unlike the conventional linear combiner (Adaline) approach, the new algorithm is based on an objective function often used in independent component analysis for robust tracking under impulse noise conditions. However, the accuracy and speed of convergence of this algorithm depend on the choice of step size of the filter and its adaptation. Instead of choosing the step size η and the parameter β of the cost function by trial and error, an adaptive particle swarm optimization technique is used alternatively to obtain both η and β to reduce the error between the observed voltage or current samples and the estimated ones. Using the optimized values, the amplitude and phase of the fundamental and harmonic components are estimated. Further, the extracted fundamental component is used to estimate any frequency drift of the power system recursively using an optimized error function obtained from three consecutive voltage samples. To test the effectiveness of the algorithm, several time-varying power system signals are simulated with harmonics, interharmonics, and decaying dc components buried in noise with low signal-to-noise ratio (SNR) and are used to estimate the frequency and harmonic components. This approach will be useful in islanding detection of a distributed generating system.     

基于特征值分解和快速独立分量分析的谐波/间谐波检测方法

针对电力系统谐波污染问题,提出了基于特征值分解和快速独立分量分析(FastICA)的谐波/间谐波检测算法。该方法在不需要任何先验知识的情况下,将单道电力系统混合信号通过时间延迟构造出多道观测信号,对其自相关函数进行特征值分解确定原谐波/间谐波信号中源信号频率成分的个数,确定观测信号矩阵的阶数,再利用FastICA算法对谐波/间谐波信号中各个频率成分进行分离提取,借助频谱分析得到各个成分的频率估计。在此基础上,借助最小二乘法对谐波/间谐波信号进行幅值和相位估计。通过仿真实验与其他经典算法比较,充分说明了所提出算法的可行性、准确性和有效性。     

Design of efficient fractional operator for ECG signal detection in implantable cardiac pacemaker systems

A low power and high-performance digital electrocardiogram (ECG) detector has become a basic requirement in modern implantable cardiac pacemakers. A fractional operator-based digital ECG detector for modern pacemaker systems is proposed in this work. Instead of conventional thresholding, an adaptive slope prediction threshold is utilized for the detection of ECG peaks. A stochastic search-based algorithm, namely, cuckoo search algorithm, is used to design an optimal fractional operator that is used for ECG denoising. It has been found that the proposed adaptive slope prediction threshold increases the QRS complex detection performance. A low detection error rate (DER) ranges from 0.01% to 0.56%, positive predictivity (P+) ranges from 99.32% to 99.98%, sensitivity (Se) ranges from 99.45% to 99.98%, and a detection accuracy (Acc) ranges from 99.43% to 99.96% for different databases are achieved for the proposed ECG detector, which is better compared with the existing ECG detectors. The proposed design of fractional order operator based on the lattice wave digital filter (LWDF) requires a minimum number of the multipliers for its structural realization.     

基于自适应滤波器的电网谐波检测

为了快速、有效地检测出三相电力系统中的谐波、负序和零序电流,文章在传统自适应检测方法的基础上提出了基于自适应滤波器的电网谐波检测方法。采用最小方差理论和平均原理对自适应滤波算法进行分析,对信号中产生的延时和相移予以有效补偿。文中分析了检测电路的频率特性,且理论上证明了系统的稳定性。检测电路采用锁相环产生参考正交正弦电压信号和在积分器前串接低通滤波器的方法大大提高了系统的检测精度和动态响应特性。仿真结果证明了该检测方法的正确性和优越性,此方法同样适用于电压各分量的检测。     

计及衰减直流分量的基频电压半周期检测

衰减直流(DDC)分量、高次谐波等干扰信号的存在,使得对电网畸变信号中基频分量的幅值、相位检测存在一定误差,其中DDC分量的时间常数通常超过45 ms,持续时间较长.为此,文中首先针对畸变信号中DDC分量提出一种半周期四点采样检测算法,缩短了DDC分量的检测响应时间.其次,针对同时含有DDC分量与高次谐波的畸变信号,提...     

基于GPRS通信的高精度配电网实时监控终端设计

针对智能配电网监控系统的特点,结合GPRS远程通信技术,采用ARM7处理器LPC1768、GPRS模块MC37i和24位高精度A/D采集芯片ADS1278,设计了高精度的配电网实时监控终端,解决了传统配电网监控终端传输距离短、测量精度不高、保护动作响应缓慢、实时性差等问题。在讨论了整体设计方案的基础上,介绍了软、硬件模块的设计方法,实现了实时遥测、遥信、遥控和快速故障保护等功能。测试结果表明,该装置测量精度高、通信距离远、数据传输稳定可靠、故障保护响应迅速、实时性强。     

基于Sage-Husa卡尔曼滤波的三相电压暂降检测

针对三相三线制系统,提出一种检测三相电压暂降的新方法。通过瞬时对称分量法在αβ静止坐标系下建立离散检测模型,引入90°超前移相算子,解耦了暂降电压正序、负序分量的检测;采用Sage-Husa卡尔曼滤波算法实现移相算子,产生正交分量,同时表现出算法自身的滤波功能;引入欧氏空间距离定位故障发生及恢复时刻,并重置卡尔曼滤波器参数,加快检测响应速度。仿真结果表明,所述方法在响应速度和稳态精度方面具有优越性,证明了该方法的可行性和有效性。     

基于数字图像处理的液位测量系统的研究与实现

基础设备的数字化是智慧电厂发展的基本要求。为实现基地式液位仪表的远程监视与自动读数,提出了一种基于数字图像处理的液位测量系统。图像处理算法包括颜色阈值分割、改进的Canny边缘检测、模板匹配,并提出窗口搜索峰值检测算法,分别对采集图像进行仪表定位、液位分界面提取、数字识别和摄像头自标定,从而实现液位图像的数字化。同时,该测量系统具有自适应中值滤波、直方图分析和透视失真自矫正功能,增强了测量的抗干扰性和自适应性。经实验室试验,测量系统的有很好的准确性,能维持原有仪表的精度等级。     

舰艇舱室内爆炸超压检测系统设计及其动态验证

为了在有限的舰艇舱室空间内实现对爆炸冲击波超压的有效检测,设计了一种体积小、功耗低、响应快的集成模块化自适应存储式舰艇舱室内爆炸超压检测系统。概述了系统的基本组成,给出了超压检测系统的硬件组成,主要包含了电源硬件模块、信号调理模块、存储模块以及触发模块等部分。设计了一个内径为0.8 m,长0.8 m,壁厚12 mm的舰艇舱室模拟装置,并在5 g及6.8 gTNT柱形装药下进行了内爆炸动态验证试验,分别测得4个不同位置处的超压数据信号,基于内爆炸分析理论,证实了超压数据的合理性,同时也验证了本文设计的超压检测系统的有效性。该系统为深入研究舰艇舱室内爆炸冲击波流场的变化、荷载分布及舰艇机电设备的防护提供了技术支撑。     

基于小波变换和分形理论的电能质量扰动监控系统

介绍了实时电能质量扰动监控系统的结构,详细说明了该系统硬件和软件各个构成模块的工作原理。为实现实时在线监控电能质量扰动,首先需检测出扰动信号,然后进行分析处理。在扰动检测模块中,采用自适应线性神经元实现了对各种扰动的检测,将检测出的扰动信号送入分类模块,采用离散小波多分辨率分析提取不同尺度下的能量分布特征,同时采用分形几何学提取局部方差维数,将二者结合共同构成扰动信号的特征矢量。将提取的特征矢量送入概率神经网络实现网络训练和扰动分类。通过模拟数据测试,该系统的分类率可达到90%。另外,该系统是在CAN总线变电站自动化系统上实现的,通过调整数据的传输格式也可将其应用到其它传输平台的变电站,实现对电能质量扰动的监控。     

采用奇异值梯度信息的暂态电能质量扰动自适应检测方法

为了满足对电网非平稳扰动信号快速、准确分析的要求,提出了一种采用奇异值梯度信息的暂态电能质量扰动检测新方法。通过滑动窗奇异值分解(SVD)方法提取信号的变化特征、降低噪声干扰,并通过奇异值梯度求取扰动指示信号,得到初步定位结果。提出无参自适应阈值,进一步抑制噪声干扰并实现对暂态扰动信号的检测定位。所提算法原理简单,无需进行前置滤波及参数调节。一系列仿真试验的对比分析结果表明,所提算法定位准确、抗干扰能力强,对过零点扰动也有较好的检测效果。通过对变电站实际暂态扰动数据的检测分析,进一步验证了所提算法的有效性。     

A nonintrusive fast residential load identification algorithm based on frequency‐domain template filtering

User‐side load monitoring is a key technology to realize smart utilization of electric power. Since the traditional intrusive load monitoring involves a comparatively large economic cost and execution complexity, this paper studies a way to rapidly identify residential power load in a nonintrusive monitoring mode. A template‐filtering‐based nonintrusive, rapid residential load identification algorithm is proposed, which is based on frequency‐domain analysis of current signals, in combination with the current model when the nonintrusive monitoring load is in operation, and by making use of the fact that the spectrum components of the current signals working independently are completely contained in the hybrid current spectrum. Characteristic currents of the various loads in the power grid are acquired a priori to establish the characteristic filter, and 0–1 valuation is performed on their spectrum components to get the template filter. The template filter is then used to filter the hybrid current signals captured in the nonintrusive mode, and the operation status of the loads is judged and determined after quantification of the filtered frequency components. For the same type of load under different operating conditions, the template filter can be commonly used. Efficiency of the algorithm is verified by making use of the actually collected power consumption data, which is able to accurately identify the load operation status. Furthermore, the algorithm is shown to be highly efficient and can be realized via fast Fourier transform (FFT), and its hardware packaging can be easily realized. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于变分模态分解与Teager能量算子的谐波/间谐波检测方法

为了提高电力谐波信号中谐波/间谐波的检测精度,提出一种基于变分模态分解(VMD)与Teager能量算子相结合的检测新方法。利用相关系数法来确定VMD算法中的模态分解个数K;采用VMD对谐波/间谐波信号进行分解,得到一系列IMF分量;利用Teager能量算子对IMF分量进行解调分析,能够得到分量的瞬时幅值和频率,同时根据时频图中瞬时频率突变点,可准确定位暂态谐波/间谐波的起止时刻。在信噪比较低的情况下,将集合经验模态分解(EEMD)、VMD分别与Teager能量算子相结合进行谐波/间谐波检测的对比。仿真实验对比表明文中所提方法能将稳态、暂态谐波信号进行有效的分离,同时具有较高的检测精度和较好的噪声鲁棒性。     

基于经验小波变换的电力系统谐波检测方法

针对噪声干扰下的稳态以及暂态谐波检测问题,首次提出一种基于经验小波变换的电力系统谐波检测方法。首先利用经验小波变换从电力谐波信号中提取出一组具有紧支撑频谱的调幅-调频分量,实现各次谐波与基波信号的分离。接着对分离出的谐波分量进行Hilbert变换,从而获取各次谐波的幅值和频率检测参数以及暂态谐波的扰动起止时刻。对多类谐波信号的仿真结果表明,所提方法有效避免了传统Hilbert-Huang变换存在的模态混叠问题,即使在低信噪比下也能实现多频谐波信号的自适应分解,在确保各类参数检测结果精度的同时,兼具良好的噪声鲁棒性和检测实时性。     

直流电能表计量信号自适应降噪方法

直流配电网包含DC/DC变换器等电力电子器件,非线性特性显著,导致直流输出端电压、电流信号存在大量纹波,需通过滤波降噪处理提升直流电能计量的准确性。针对现有的滤波降噪方法参数设置缺乏优化、滤波降噪效果尚待提升问题,本文提出基于自适应变分模态分解与小波阈值去噪相结合的直流电能计量数据降噪方法。建立输出端直流电压、电流信号变分模态分解的参数最优化模型,并联合互信息分析,实现原始信号的有效模态分量与噪声模态分量的自适应区分。在此基础上,建立以信噪比、均方根误差、平滑度、相关系数复合评价指标最优的小波阈值去噪参数最优化模型,实现噪声模态分量的最优滤波降噪。通过实测数据计算分析,验证所提方法的有效性。     

Adaptive Optimal Kernel Time–Frequency Representation Technique for Partial Discharge Ultra-high-frequency Signals Classification

An intelligent system for automatic partial discharge pattern recognition is proposed using adaptive optimal kernel time-frequency representation and a fuzzy k-nearest neighbor classifier. The adaptive optimal kernel technique is employed to acquire the joint time-frequency information for partial discharge signals, which is characterized by the adaptive optimal kernel amplitude matrix. A new feature extraction algorithm, i.e., non-negative matrix factorization aided principal component analysis, is proposed to solve the difficulties of principal component analysis for feature extraction of partial discharge adaptive optimal kernel amplitude matrices due to the high dimensionality. Using an ultra-high frequency detector, 600 partial discharge signals sampled from 4 categories of typical artificial defect models in the laboratory are employed for testing. It is shown that the maximum classification accuracy of 94.33% is obtained considering different non-negative matrix factorization parameter r and various non-negative matrix factorization iterations T. Also, the classification performance of the non-negative matrix factorization–principal component analysis features is superior to that of principal component analysis features extracted from original partial discharge signals, two-dimensional non-negative matrix factorization features and phase-resolved partial discharge statistical operators. The proposed technique can be used for partial discharge pattern recognition based on ultra-high-frequency detection arrangements.     

基于HHT风力发电系统谐波检测方法的研究

对风力发电系统进行准确的谐波检测具有重要意义。为了克服FFT方法与小波分析方法的缺点,文中提出利用HHT对信号的自适应特性,将HHT用于谐波分析。将谐波信号进行经验模态分解,得到一系列经验模态函数IMF;由于不同的IMF对应不同的谐波分量,通过对每个IMF分量进行Hilbert变换,最终可以得到各次谐波分量;并与前两种方法进行对比。文中介绍的方法在时域和频域同时具有很高的检测精度,为风电谐波检测提出了一种新的思路。     

一种面向心电信号处理的奇异谱分析改进算法

心电图(ECG)作为人体的关键生理信号被广泛应用于医疗领域,但在采集过程中心电信号容易受到噪声干扰而影响 信号质量。为此,设计了一种奇异谱分析(SSA)的改进算法用于心电信号降噪处理。奇异谱分析改建算法是在 SSA中的主 元重组(grouping)阶段引入逻辑回归(LR) 算法,将主元重组方式改进为自动重组,实现面向心电信号的 SSA 自监督降噪处 理。使用基于 AD620 的心电信号采集装置,构建53条心电信号测试集进行验证,使用奇异谱分析的改进算法,主元自动选择 的准确性为98.68%,重构的心电信号信噪比(SNR)由10.43 dB平均提高到20.17 dB,能够有效提取出清晰的PQRST 波,使 其在医疗领域心电信号检测与降噪方面具有很好的实用化前景。     

基于静态阈值的无线体域网压缩感知心电降噪重构

心电监测作为无线体域网的一种重要应用,对心电信号重构精度要求较高,并且无线体域网中存在低功耗问题。现有的心电信号压缩感知重构算法虽然降低了功耗,但并未充分利用心电信号频域特性,造成重构精度不高。提出了一种基于静态阈值的无线体域网压缩感知心电降噪重构方法。该方法利用压缩感知理论,在传感器节点利用固定矩阵对心电信号进行观测,观测值被发送至汇聚节点后,再利用基于静态阈值的重构算法对心电信号进行降噪重构。仿真结果表明,该方法具有信号重构精度高、速度快和降噪性能好的优点。