基于VSS-CDFE的三相非平衡电力系统频率无偏估计

为了实现含噪三相非平衡电力系统高精度频率无偏估计,引入了复数域直接频率估计(CDFE)算法,分析其原理并对其进行了改进。CDFE算法基于正弦信号的线性预测,求取误差函数的瞬时平方值关于频率的偏导数,并以该值作为频率估计的更新值。在此基础上,进一步提出变步长CDFE(VSS-CDFE)算法,根据最速下降法则动态更新步长因子来代替CDFE算法的固定步长。仿真分析及实验结果表明,在噪声干扰下,VSS-CDFE算法可以准确地对基于复数建模的三相非平衡电力系统进行频率追踪,其估计均方误差和理论值相吻合。相比CDFE算法,VSS-CDFE算法在相同的收敛速度下,估计均方误差更小,在相同的估计均方误差下,收敛速度更快。     

一种新的变步长自适应谐波检测算法

提出了一种新的变步长最小均方(LMS)自适应谐波检测算法,并将其应用于有源电力滤波器中。该方法根据误差信号的时间均值估计来调节递推算法的步长,其优越性在于：即使在待检信号的信噪比(SNR)较低的情况下,也能够保证谐波检测过程既具有较快的动态响应速度,又保持较小的稳态失调。通过递推公式系数的选择,可以对系统的收敛速度与稳态失调进行更灵活的控制,而不像定步长 LMS 算法那样必须在两者性能上进行折中选择。仿真和实验结果亦证明了理论分析的有效性。     

高阶QAM实时多域测试多模式自适应盲均衡技术研究

提出了一种全新的宽带通信信号实时多域分析通用架构,详细介绍了该架构下信号分析的基本原理。在这种架构的基础上,通过加载不同的算法,不仅能够实现各种宽带通信信号高精度实时宽带频谱分析,而且还能同时实现宽带通信信号时域、调制域等多域联合分析。针对宽带高阶正交幅度调制（QAM）通信信号实时多域分析,详细讨论了面向测试的基于GMMA和DDLMS双模自适应盲均衡算法。系统仿真结果证明：相比GMMA自适应盲均衡算法,双模自适应盲均衡算法收敛速度明显提高,256QAM信号均衡后输出残余码间串扰（ISI）改善提高了10dB;同时通过实验验证,采用20MHz实时分析带宽对码率为6.4MSps的宽带256QAM信号进行实时多域分析,误差矢量幅度（error vectorm agnitude,EVM）测试误差小于2%。     

Filtered gradient active fuzzy neural network noise control in an enclosure backed by a clamped plate

The performance of conventional linear algorithms in active noise control applications deteriorates facing nonlinearities in the system mainly because of loudspeakers. On the other hand, fuzzy logic and neural networks are good candidates to overcome this drawback. In this paper, the acoustic attenuation of noise in a rectangular enclosure with a flexible panel and five rigid walls is presented both theoretically and experimentally using filtered gradient fuzzy neural network (FGFNN) error back propagation algorithm in which the secondary path effect is implemented in derivation of updating rules. Considering this effect in updating rules leads to faster convergence and stability of the active noise control system. On the other hand, the primary path in the investigated system comprises an identified nonlinear model of loudspeaker inside the aforementioned box, parameters of which vary with the input current. The loudspeaker is identified using series‐parallel neural network model identification method. As a comparison, the performance of filtered‐x least mean squares and FGFNN algorithms are compared. It is observed that FGFNN controller exhibits far better results in the presence of loudspeakers with nonlinear behavior in primary path.Copyright © 2012 John Wiley & Sons, Ltd.     

用于主动噪声控制系统的高性能算法结构

本文针对传统的自适应滤波算法降噪性能差、收敛速度慢以及应对突变能力不足等问题,提出了基于改进的方程误差算法和镜像优化算法。其中,改进的方程误差算法在FURLMS算法基础上进行离线二次路径建模,解决了降噪性能和收敛速度的问题。为了提高系统应对突变的能力,该算法在FURLMS算法基础上进行了镜像优化。结果表明,本文提出的两种算法在系统频率为250 Hz左右范围时,均方误差可稳定在-20dB,提出的改进方程误差算法和镜像修改算法分别有28dBA和30dBA的噪声衰减效果。     

Steiglitz–McBride adaptive notch filter based on a variable‐step‐size LMS algorithm and its application to active noise control

This paper proposes a new Steiglitz–McBride (SM) adaptive notch filter (SM‐ANF) based on a robust variable‐step‐size least‐mean‐square algorithm and its application to active noise control (ANC). The proposed SM‐ANF not only has fast convergence but also has small misadjustment. The variable‐step‐size algorithm uses the sum of the squared cross correlation between the error signal and the delayed inputs corresponding to the adaptive weights. The cross correlation provides robustness to the broadband signal, which plays the role of noise. The proposed SM‐ANF is computationally simpler than the existing Newton/recursive least‐squares‐type ANF. The frequency response of the new SM‐ANF has a notch depth of about ?25 dB (for each of the three frequencies considered) and has spectral flatness within 5 dB (peak to peak). This robust notch filter algorithm is used as an observation noise canceller for the secondary path estimation of an ANC system based on the SM method. The ANC with proposed SM‐ANF provides not only faster convergence but also an 11‐dB improvement in noise attenuation over the SM‐based ANC without such a SM‐ANF. Copyright © 2015 John Wiley & Sons, Ltd.     

基于改进WOA的鲁棒增广扩展Kalman滤波器设计

本文针对一类非线性系统,提出基于广义系统的鲁棒增广扩展Kalman滤波器,结合改进鲸群优化算法寻优系统噪声,以精确估计系统状态量以及并发执行器和传感器故障。首先,视故障为系统的状态变量,建立广义系统,将非线性系统的故障估计转化为非线性广义系统的状态估计。其次,提出鲁棒上界以降低线性化误差对估计精度的影响。然后,利用改进鲸群算法寻优系统噪声,以优化鲁棒增广扩展Kalman滤波器。最后,给出F-16飞机的纵向运动数值模型,使用本文方法与自适应无迹Kalman滤波器以及基于鲸群算法的鲁棒增广扩展Kalman滤波器进行对比仿真,仿真结果表明,相较于其他两种算法,本文方法的故障估计均方根误差降低了50%左右,验证了其优越性。     

脉冲噪声环境下混沌扩频时延估计算法研究

针对传统时延算法面对脉冲噪声时运算结果峰值旁瓣比较低，且存在误判点较多难以判断的问题，提出了一种新型加权高斯相关熵时延估计方法，并将该方法应用于电缆故障定位的仿真模型中。仿真结果表明，与现有的方法相比，不仅可以在脉冲噪声环境下获得良好的时延估计效果，而且在强脉冲噪声干扰下依旧能够保持较高的定位精度。在不同强度的脉冲噪声背景下，其运算结果相比其他3种方法主峰值旁瓣比绝对值增加0.020 3 dB以上，误判峰值与故障点峰值比减少了0.053 9以上，均方值误差减少了1.863 6 m以上。     

一种改进ip-iq谐波电流检测算法

为了更加快速、精确地检测出三相电力系统中谐波电流,基于瞬时无功功率理论,提出了一种改进谐波电流检测算法.该算法利用一种变步长LMS算法来实现ip-iq理论中低通滤波器的功能,用当前误差信号和上一次误差信号归一化的自相关估计来进行步长迭代.与传统ip-iq算法相比,该算法在不降低检测精度的前提下,具有更快的动态响应性能....     

改进变步长LMS算法在软起动谐波检测中的应用

针对现有自适应变步长最小均方(LMS)谐波电流检测算法在低信噪比环境中易受干扰影响,提出一种改进自适应变步长LMS算法。该算法将误差信号与前一工频周期误差信号的差值作为反馈量,结合箕舌线函数构造出随动的动态因子,将此动态因子也作为调节权值的动量因子,利用自相干估计误差控制步长。该算法折中考虑收敛速度和稳态精度,并有效降低噪声信号的干扰。通过对电机软起动器工作过程中产生的周期重复性谐波进行检测和分析,证明了该谐波电流检测算法的可行性。     

Power system frequency estimation using least mean square technique

Frequency is an important parameter in power system monitoring, control, and protection. A least mean square (LMS) algorithm in complex form is presented in this paper to estimate power system frequency where the formulated structure is very simple. The three-phase voltages are converted to a complex form for processing by the proposed algorithm. To enhance the convergence characteristic of the complex form of the LMS algorithm, a variable adaptation step-size is incorporated. The performance of the new algorithm is studied through simulations at different situations of the power system.     

自适应变步长BP算法及转速辨识控制

基于反电动势估算的ANN速度辨识模型,采用转子磁场定向的矢量控制方法,构建了一个无速度传感器异步电机调速系统。在保证反电动势误差函数能量最小的前提下,提出了自适应变步长BP算法,加快了收敛速度,缩短了调节时间。系统实验结果表明:速度辨识精度高,系统稳定性好,且具有良好的静、动态性能。     

基于自适应FIR预测滤波器的谐波检测

针对现阶段有源电力滤波器畸变电流检测方法存在工频周期时延、计算量大等不足的问题,提出了基于自适应有限脉冲响应(FIR)预测滤波器的谐波实时检测系统。论述了自适应滤波器谐波检测原理并利用变步长的最小均方算法(LMS)对所需检测信号进行预测,而预测算法的步长因子是根据误差信号的时间均值估计来调节的,即当滤波器的预测系数远离最优解时,步长比较大,以加强动态响应速度和对时变系统的跟踪能力;当滤波器的预测系数接近最优解时,步长比较小,以获得较小的稳态误差。对该预测法采用MATLAB进行了仿真和实验,结果表明当电流突变时,该方法仍然能够在一个周期内正确预测出未来时刻的谐波电流值。     

State estimation of discrete‐time systems with arbitrarily correlated noises

In this paper, the state estimation problem for discrete‐time systems is considered where the noises affecting such systems do not require any constraint condition for the correlation and distribution, that is, the noises can be arbitrarily correlated and arbitrarily distributed random vector. For this, two filtering algorithms based on the criterion of linear minimum mean‐square error are proposed. The first algorithm is an optimal algorithm that can exactly compute the linear minimum mean‐square error estimate of system states. The second algorithm is a suboptimal algorithm that is proposed to reduce the computation and storage load of the proposed optimal algorithm. Computer simulations are carried out to evaluate the performance of the proposed algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

用于电压闪变包络跟踪的改进RLS算法

电压闪变是严重的电能质量问题之一,本文提出采用基于可变遗忘因子的高阶累积量递推最小二乘(RLS)算法跟踪电压闪变包络。基于高阶累积量的误差准则取代传统的基于二阶统计量的误差准则,使算法不受任何高斯噪声的影响;可变遗忘因子的应用,使得算法能够快速跟踪包络的变化,又能在稳态情况下具有较好的收敛性能。Matlab仿真分析结果表明,在高斯噪声污染存在的情况下,本文算法相比传统RLS算法具有更高的估计精度,能够在噪声中准确地检测出电压闪变包络。     

ANC系统次级通道背景噪声的处理方法

由于电力变压器ANC系统次级通道背景噪声的影响,使其辨识参数不能反映次级通道本身特性,影响整个降噪系统的降噪效果和收敛性能。论文首先以Fx-LMS算法为例,分析了辨识误差对算法收敛系数的影响。然后提出了一种次级通道背景噪声的去噪新方法,即将分时测量法与信号相关性分析结合,极大限度抑制了与激励信号频段相近的背景噪声影响。最后通过仿真计算证明去除背景噪声的效果明显,次级通道辨识精度显著提高。     

基于改进自适应谐波检测法的有源电力滤波器

针对传统自适应谐波检测方法在收敛速度和稳态精度之间存在的矛盾,提出了一种改进的新型自适应谐波电流检测方法。该方法基于自适应噪声对消理论,通过引入动态因子项自适应地调整算法的步长,引入动量项加快了权值的收敛,引入静态项和自相关误差项消除了不相关噪声序列的干扰,很好地解决了收敛速度与稳态精度的矛盾,进一步提升了谐波检测效果。仿真及实验结果证明了该改进检测法的可行性和有效性。     

基于辅助变量的永磁同步直线电机系统辨识研究

针对永磁同步直线电机系统在有色噪声干扰下的辨识问题,提出了一种基于辅助变量的模型参数辨识方法。分析并建立了永磁同步直线电机的数学模型和系统的开环传递函数,引入辅助变量对标准的递推最小二乘法进行改进,对夹杂有色噪声数据的系统模型进行参数辨识。同时,基于固定模型的变回归估计方法（FMVRE）辨识了系统中可能存在的纯延时环节因子。仿真结果表明:在有色噪声影响下,辅助变量递推最小二乘法的辨识精度要高于标准的递推最小二乘法,各参数估计值的误差均在4%以下,并且额外增加的计算量较少。辨识实验的结果也证明了辅助变量递推最小二乘法能够在有色噪声干扰下辨识出较为精确的系统模型。     

有源电力滤波器的动态因子LMS谐波检测方法

针对自适应谐波电流检测方法收敛速度与稳态精度需折中考虑的问题,采用单相并联有源电力滤波器(APF)作为硬件平台.基于自适应噪声对消技术,提出一种用于APF谐波检测的动态因子最小均方(LMS)算法.通过引入动量项,并利用误差信号在相邻时刻的时间均值估计来控制步长更新,极大加快了算法的收敛速度.采用动态因子项对误差再次进行...     

Proposed network structures and combined adaptive algorithms for electrocardiogram signal denoising

An electrocardiogram (ECG) signal is a record of the electrical activities of heart muscle and is used clinically to diagnose heart diseases. An ECG signal should be presented as clear as possible to support accurate decisions made by doctors. This article proposes different combinations of combined adaptive algorithms to derive different noise-cancelling structures to remove (denoise) different kinds of noise from ECG signals. The algorithms are applied to the following types of noise: power line interference, baseline wander, electrode motion artifact, and muscle artifacts. Moreover, the results of the suggested models and algorithms are compared with those of conventional denoising tools such as the discrete wavelet transform, an adaptive filter, and a multilayer neural network (NN) to ensure the superiority of the proposed combined structures and algorithms. Furthermore, the hybrid concept is based on dual, triple, and quadruple combinations of well-known algorithms that derive adaptive filters, such as the least mean squares, normalized least mean squares and recursive least squares algorithms. The combinations are formulated based on partial update, variable step-size (VSS), and second iterative VSS algorithms, which are considered in different combinations. In addition, biased NN and unbiased linear neural network (ULNN) structures are considered. The performance of the different structures and related algorithms are evaluated by measuring the post-signal-to-noise ratio, mean square error, and percentage root mean square difference.