基于可变遗忘因子广义RLS算法的频率估计

传统的递推最小二乘(RLS)算法有良好的抑制噪声的能力,但在非稳态环境下跟踪能力弱,导致误差大.RLS和Kalman滤波之间存在一一对应的关系,引入Kalman滤波的一步预测估计和新的状态转移矩阵,可以得到广义的RLS算法,该算法改进了跟踪能力.同时,考虑到加权遗忘因子对算法的收敛速度和跟踪能力也有很大影响,故在广义RLS算法中再引入可变的遗忘因子,以确保对时变参数的快速跟踪能力和小的参数估计误差.对基于可变遗忘因子的广义RLS自适应算法和按指数加权的传统RLS算法进行了仿真比较,分析了在稳态下加入谐波、输入幅值变化、输入频率变化等情况下,2种方法所得的频率估计值和均方误差,结果显示所提方法在精度和收敛速度上都更优越.     

一种用于电力系统间谐波谱估计的高分辨率算法

本文提出一种基于改进高阶累积量求根MUSIC法的间谐波谱估计新方法。该算法采用4阶累积量来抑制高阶白噪声和高斯噪声,同时,针对传统4阶累积量分辨率低的特点,通过将4阶累积量的对角切片转化为修正协方差阵的线性组合,从而保证算法的高分辨率;并通过构造MUSIC多项式,从而使算法噪声抑制能力进一步增强。仿真结果表明,该方法不仅具有较高的频率分辨率,而且具有较强的稳健性,在噪声污染的情况下能检测出各次谐波和间谐波的频率信息。     

基于高阶累积量Burg自适应算法的间谐波估计

Burg算法适合处理间谐波信号,但是在有噪声的环境下,会产生错误的谱估计结果。采用一种基于高阶累积量的Burg自适应算法估计间谐波信号,该方法不受高斯噪声的影响。首先,利用高阶累积量对高斯噪声不敏感的特性,改进Burg算法的预测误差标准。其次,为了降低引入高阶累积量而增加的计算量,反射系数的求取采用递推形式。最后,利用谐波信号的4阶累积量对角切片求取各成分的幅值。仿真结果表明,所提出的算法明显改进了Burg算法的谱估计性能,能够在噪声环境下获得准确的间谐波参数估计。     

噪声情况下的时变间谐波谱估计

间谐波幅值远小于基频或其它整数倍谐波的幅值,使其对噪声非常敏感,噪声往往会将这类微弱信号淹没。另一方面,实际间谐波频谱是随时间变化的,应看作随机信号来处理。该文提出一种基于4阶累积量的可变遗忘因子递推最小二乘法(cumulants recursive least square-variable forgetting factor,CRLS-VFF),将间谐波信号看作一个时变自回归(auto-regressive,AR)模型,利用参数化谱估计方法分辨率高的优点,将间谐波谱估计问题转化为时变AR参数的估计。4阶累积量可抑制任何高斯噪声,保证算法的频率分辨率;可变遗忘因子提高了算法跟踪时变参数的能力。对根据间谐波特点构建的仿真模型及典型的间谐波源——变频装置产生的信号进行仿真,结果证明：该方法能在噪声情况下准确估计出时变间谐波的频谱。     

基于独立分量分析的电压闪变检测方法

独立分量分析利用信号的高阶统计量快速准确地实现信号的分离和恢复。提出利用独立分量分析和同步滤波相结合的电压闪变检测方法。首先,采用同步解调的方法将包络信号和工频电压相乘的形式转变为包络信号和中心频率为100 Hz的双边带调幅信号的线性组合;然后依据负熵最大化的独立性准则和基于固定点的Fast-ICA算法进行盲信号分离;对分离得到的包络信号进行幅值修正,实现真实信号的估计。仿真结果表明该算法能准确分离平稳和非平稳的电压闪变信号的包络信号,且对幅值和频率的检测精度高。     

基于Teager-Kaiser能量算子Rife-Vincent窗频谱校正的电压闪变测量

分析了Teager-Kaiser能量算子的构成和Rife-Vincent窗的旁瓣特性,通过Teager-Kaiser算子快速提取电压闪变包络,将包络参数进行Rife-Vincent窗改进FFT谱分析与校正,简化IEC推荐的闪变测量过程,提出并建立了基于Teager-Kaiser能量算子Rife-Vincent窗频谱校正的电压闪变测量算法。仿真试验结果表明,本文提出的算法能有效克服单一频率调幅波频率变化、多频率成分调幅波、电网基波频率波动、谐波与间谐波以及白噪声对检测结果的影响,与传统电压闪变检测方法相比,设计实现简单、计算量小,测量结果精确、稳定,可用于电压闪变参数的在线监测。     

递归最小二乘法追踪电压闪变包络线

应用常规递归最小二乘法(RLS)同时估计2个或2个以上变量时容易产生较大误差,改进RLS根据情况选择遗忘因子的个数:利用前一次估计的结果,把误差分散到不同参数,然后各自选用合适的遗忘因子,使各参数以不同的比率变化,优化追踪结果。通过Matlab仿真,分别用常规RLS和改进RLS追踪电压闪变的包络线和初相角,用改进RLS追踪单频、多频闪变及其加噪后的包络线幅值,并对包络线进行了快速傅里叶变换分析。结果证实该方法提高了准确性。     

基于改进RLS算法的时变幂函数负荷模型参数辨识

考虑到实际配电网负荷的组成会随着用户行为、天气状况以及系统运行方式而发生变化,负荷模型的参数与外界影响因子之间的关系是复杂且非线性的,并应随时间发生变化,因此,提出采用考虑时变性的幂函数模型来描述配电网负荷和电压随时间变化的情况。此外,为了提高模型参数辨识的准确性,提出基于三阶累积量的可变遗忘因子递推最小二乘法,用于自适应跟踪模型的时变参数。基于数字仿真算例和电网实际采样数据,对改进算法和传统递推最小二乘法的负荷模型参数辨识结果进行了仿真比较,从各类误差评价指标来看,改进算法估计精度优于传统递推最小二乘法,具有良好的跟踪性能和抑噪性能。     

一种电压闪变实时检测的新方法

提出了一种基于数学形态学均值滤波与能量算子包络检测的电压闪变实时检测的新方法。区别于其它去噪滤波算法,改进的数学形态学的滤波器只含有加减和一次除法运算,而能量算子包络检测只需要对被测波形的三个样本进行两次乘法和一次减法运算,使得所提出的算法快速、简洁。仿真结果表明,所提算法能够准确地检测出闪变波形的包络,并且克服了Teager能量算子算法对噪声和突变的敏感性,适合电压闪变的实时检测。     

一种电压闪变实时检测的新方法

提出了一种基于数学形态学均值滤波与能量算子包络检测的电压闪变实时检测的新方法.区别于其它去噪滤波算法,改进的数学形态学的滤波器只含有加减和一次除法运算,而能量算子包络检测只需要对被测波形的三个样本进行两次乘法和一次减法运算,使得所提出的算法快速、简洁.仿真结果表明,所提算法能够准确地检测出闪变波形的包络,并且克服了Teager能量算子算法对噪声和突变的敏感性,适合电压闪变的实时检测.     

Convergence analysis of the RLS identification algorithm with exponential forgetting in stationary ARX‐structures

The recursive least‐squares (RLS) identification algorithm is often extended with exponential forgetting as a tool for parameter estimation in time‐varying stochastic systems. The statistical properties of the parameter estimates obtained from such an extended RLS‐algorithm depend in a non‐linear way on the time‐varying characteristics and on the forgetting factor. In this paper, the RLS‐estimator with exponential forgetting is applied to time‐invariant Gaussian autoregressions with second‐order stationary external inputs, i.e.to Gaussian ARX‐processes. Approximate expressions for the asymptotic bias and covariance of the parameter estimates when the forgetting factor tends to one and time to infinity are given, showing that the bias is non‐zero and that the covariance function decays exponentially with a rate that is given by the forgetting factor. The orders of magnitude of the errors in the asymptotic expressions are also derived. Copyright © 1999 John Wiley & Sons, Ltd.     

Estimation techniques for voltage flicker envelope tracking

Flicker envelope tracking has many important applications in distribution systems for either flicker meters or flicker compensators. This paper introduces a new application of the adaptive linear neuron (ADALINE), and the recursive least square (RLS) algorithm for flicker envelope tracking. The ADALINE is characterized by a light computational demand, unlike that of existing techniques; the RLS algorithm betters the ADALINE by its fast convergence and robust tracking performance. Both the ADALINE and the RLS algorithm give accurate results even under rapid dynamic changes. The paper investigates the effects of different parameters on the ADALINE and the RLS algorithm. Extensive simulations of the proposed flicker tracking algorithms are conducted to evaluate their performance. The ADALINE and the RLS algorithm are examined by tracking the flicker produced by a resistance welder in a simple distribution system simulated in the PSCAD/EMTDC package.     

A novel variable‐length sliding window blockwise least‐squares algorithm for on‐line estimation of time‐varying parameters

Motivated by the advances in computer technology and the fact that the batch/block least‐squares (LS) produces more accurate parameter estimates than its recursive counterparts, several important issues associated with the block LS have been re‐examined in the framework of on‐line identification of systems with abrupt/gradual change parameters in this paper. It is no surprise that the standard block LS performs unsatisfactorily in such a situation. To overcome this deficiency, a novel variable‐length sliding window‐based LS algorithm, known as variable‐length sliding window blockwise least squares, is developed. The algorithm consists of a change detection scheme and a data window with adjustable length. The window length adjustment is triggered by the change detection scheme. Whenever a change in system parameters is detected, the window is shortened to discount ‘old’ data and place more weight on the latest measurements. Several strategies for window length adjustment have been considered. The performance of the proposed algorithm has been evaluated through numerical studies. In comparison with the recursive least squares (RLS) with forgetting factors, superior results have been obtained consistently for the proposed algorithm. Robustness analysis of the algorithm to measurement noise have also been carried out. The significance of the work reported herein is that this algorithm offers a viable alternative to traditional RLS for on‐line parameter estimation by trading off the computational complexity of block LS for improved performance over RLS, because the computational complexity becomes less and less an issue with the rapid advance in computer technologies. Copyright © 2004 John Wiley & Sons, Ltd.     

基于VFFRLS算法的锂电池参数辨识

动力电池性能是影响电动汽车综合性能的关键因素,因此准确辨识锂离子电池模型的参数对后续电池系统的荷电状态估计和健康状态预测至关重要。为了提高锂离子电池模型参数辨识算法的精度,以磷酸铁锂电池作为研究对象,建立电池二阶RC等效电路模型,并采用基于变量遗忘因子的最小二乘算法对锂离子电池模型进行在线参数辨识。通过搭建测试平台进行充放电实验,基于2种不同工况的实验数据,分别用文中算法、递推最小二乘算法和传统的带遗忘因子的最小二乘算法进行参数辨识,根据辨识结果估计出的端口电压与实验测试得到的实际值的误差比较来描述文中算法辨识结果的准确度。实验结果表明,基于变量遗忘因子的最小二乘算法在锂电池参数辨识方面表现出快速的收敛性和较高的估计精度。     

Experimental evaluation of envelope tracking techniques for voltage disturbances

In this paper a digital signal processor (DSP) based real time voltage envelope tracking system is developed and examined. The ADAptive LINEar neuron (ADALINE) and the Recursive Least Square (RLS) algorithms are adopted for envelope tracking. The proposed ADALINE and RLS algorithms give accurate results even under rapid dynamic changes. The paper investigates the effects of different parameters on the performance of the ADALINE algorithm and that of the RLS algorithm. The experimental system is cantered around a Texas Instrument 16 bit fixed-point arithmetic (TMS320LF2407A) evaluation board. Both the ADALINE and the RLS tracking algorithms are developed using the DSP-assembly language. A simple voltage flicker generator is implemented to produce various voltage disturbances. Extensive tests of the proposed envelope tracking algorithms are conducted to evaluate their dynamic performance.     

基于修正遗忘因子RLS算法的谐波电流检测新方法

为提高电能质量,需对电网谐波进行补偿与抑制,其效果取决于谐波检测的精度和动态响应特性。本文考虑到有源滤波器(APF)的低信噪比特性,提出了一种基于自适应对消技术的修正遗忘因子RLS谐波电流检测算法,该算法从研究权值收敛方式出发,通过设置阈值来判断突变,使得检测过程既有较快的动态响应又有较高的检测精度。最后通过Matlab仿真分析和DSP实验,验证了本文的修正算法既克服了LMS算法初始收敛慢、精确度低,又克服了传统RLS算法的动态响应过慢的不足,证实了该方法是一种非常有效的谐波电流检测方法。