Adaptive noise reduction using numerically stable fast recursive least squares algorithm

This paper is concerned with adaptive noise reduction based on the fast recursive least squares (FRLS) algorithm. It is well known that the fast recursive least squares (FRLS) algorithm suffers from numerical instability when operating under the effects of finite precision arithmetic. Several numerical solutions of stabilization were proposed in the case of stationary signals. In this work a new version of a numerically stable FRLS algorithm (NS‐FRLS) is proposed. The stability characteristics of this new stabilized algorithm are analysed. The analysis is based on a linear model for the errors in the states of the adaptive filter. Experimental results confirm the merits of adaptive filtering with the NS‐FRLS algorithm over optimum filtering using the solution provided by Wiener–Hopf equations. Copyright © 2006 John Wiley & Sons, Ltd.     

Z-function convolution in EHV power network electromagnetic transient analysis

Time-convolution in the forms arising in transformation from the frequency-domain to the time-domain has been widely used in the earlier development of electromagnetic transient methods in power systems. Independent work has recently led to new methods based on the z-transform, and the present paper develops methods based on discrete convolution arising from transformation to the time-domain now from the z-plane. The recursive solution sequences to which this leads combines high accuracy with low computing time requirements. Checks and controls in the synthesis of transmission line forward impulse response and surge impedance functions in the z-plane ensure that these are always stable system functions, and that numerical solution procedures which include them have very high inherent stability. The formulations developed are applied to transmission line energization from an equivalent source model, and the electromagnetic transients in short-circuit fault operation. Close comparisons are made between representative solutions from standard time-convolution analysis and from the methods of the present paper.     

Combined state and parameter estimation for Hammerstein systems with time delay using the Kalman filtering

This paper discusses the state and parameter estimation problem for a class of Hammerstein state space systems with time delay. Both the process and the measurement noises are considered in the system. On the basis of the observable canonical state space form and the key term separation, a pseudolinear regressive identification model is obtained. For the unknown states in the information vector, the Kalman filter is used to search for the optimal state estimates. A Kalman filter–based least squares iterative and a recursive least squares algorithms are proposed. Extending the information vector to include the latest information terms, which are missed for the time delay, the Kalman filter–based recursive extended least squares algorithm is derived to obtain the estimates of the unknown time delay, parameters, and states. The numerical simulation results are given to illustrate the effectiveness of the proposed algorithms.     

非线性动态模拟电路故障信号仿真算法的实现

采用方波脉冲函数变换,将具有统一形式数学模型的非线性动态网络在时域内响应的Volterra级数解的递推算式变换成方波域内的数值递推算式.此法不仅为非线性模拟电路故障信号仿真算法的实现提供了新的方法,而且为该类非线性网络的分析计算开辟了新的途径.文中给出了仿真算法和非线性动态电路故障信号仿真实例,在弱非线性情况下,本例取Volterra级数的阶N=3,可得到相当精确的结果.     

On the uniqueness of the numerical solution of non‐linearly loaded lossy transmission lines

The purpose of this paper is to investigate the uniqueness of the solution of lossy lines with frequency‐dependent parameters terminated with non‐linear resistors. Several solutions that satisfy the same initial conditions may exist if the terminal resistors are locally active. In these cases the uniqueness of solution is assured adding parasitic capacitances in parallel to the voltage controlled resistors and parasitic inductances in series to the current controlled resistors. In this way, among all the possible solutions, the only one that assures the time continuity of the current and voltage waveforms at the ends of the line is captured. In the light of these results, the properties of numerical models of these distributed circuits based on convolution techniques have been studied, and conditions assuring the uniqueness of the numerical solution have been found. Numerical simulations, when based on qualitative information of this type, enable us to obtain the quantitative properties in an efficient manner. In particular, a simple numerical method that enforces artificially the time continuity of the solution is proposed to circumvent the need of adding parasitics. Copyright © 1999 John Wiley & Sons, Ltd.     

Online identification of time-delay jump Markov autoregressive exogenous systems with recursive expectation-maximization algorithm

This article considers the identification problem of the jump Markov autoregressive exogenous (JMARX) systems with unknown invariant time-delay under the framework of recursive expectation-maximization (REM) algorithm. In this article, a recursive Q-function is formulated for the JMARX systems, based on which the recursive sufficient statistics are obtained. Then, the parameter vectors, variance, transition probability matrix, and time-delay are recursively estimated. A numerical example and a simulated continuous fermentation reactor process are employed to illustrate the effectiveness of the proposed algorithm.     

Parameter estimation for time-delay systems based on the frequency responses and harmonic balance methods

Two parameter estimation methods for linear time-delay systems are proposed based on the frequency responses and the harmonic balance methods. One is the stochastic gradient gradient-based iterative (SG-GI) algorithm and the other is the recursive least squares gradient-based iterative (RLS-GI) algorithm. These two methods can estimate the unknown parameters and the unknown time delays simultaneously by giving sinusoidal signals with different angular frequencies. Furthermore, a comparative study reveals that the RLS-GI algorithm is more effective than the SG-GI algorithm. The effectiveness of the proposed algorithms is illustrated by a numerical example.     

电力系统阻尼控制中的在线递推闭环子空间辨识

提出了电力系统阻尼控制中状态空间模型的在线递推闭环子空间辨识算法。在闭环条件下,基于广域测量信息,在线地辨识了包含主导低频振荡模式的系统降阶状态空间模型,并依此在线设计和更新线性二次最优部分输出辅助区间阻尼控制器以抑制区间低频振荡模式。算法具有良好的数值稳定性和较低的时间复杂度,能够实现系统模型的递推更新和辅助阻尼控制器参数的在线调整。8机36节点系统的仿真验证了算法的有效性。     

基于卷积递归网络的电流互感器红外故障图像诊断

电力大数据中日益增多的非结构化数据为以人工诊断为主的传统处理方式提出了新的挑战。红外故障图像作为一种典型的非结构化数据,对于电力大数据的研究有着至关重要的作用。为了达到自动处理海量红外故障图像的目的,提出了一种基于卷积递归网络的电流互感器红外故障图像诊断方法。对红外故障图像首先进行超像素分割并利用其色度信息提取温度异常区域;然后采用两级联合卷积-递归神经网络,对大量样本信息进行训练学习来指导设备故障部位识别;最后依据部位信息对故障进行分类。实验结果表明,该算法鲁棒性较强,准确性较高,有效地提高了红外检测效率,为非结构化数据的特征提取分析提供了坚实的基础。     

A recursive least-squares digital distance relaying algorithm

A new digital distance relaying algorithm based on the well-known theory of least-squares is developed, and the results of testing it using numerical simulation are presented. The new technique estimates the distance to the fault and the fault resistance. To minimize the computational requirements of the digital relay, a recursive least-squares approach is used. Computer simulation results of the new least-squares algorithm seem promising, indicating that it should be considered for further testing and evaluation     

Real-time electrical variables estimation based on recursive wavelet transform

In frequency and phasor estimation algorithms, the undesired components are required to be filtered out from the original signals. In power systems, the undesired components are the decaying dc offset and harmonics. These components could cause delay in algorithm convergence time and deviation from the desired results to a great extent. This paper proposes a new recursive algorithm for accurate and fast estimation of the instantaneous electrical variables such as frequency, amplitude and phase angle. The new algorithm provides an improvement over the existing recursive wavelet transform and, therefore, it is called IRWT. The IRWT performance is compared with the commonly used full-cycle discrete Fourier transform (DFT) and the recursive wavelet transform (RWT) methods. Since it uses a special mother wavelet function, it reduces computational complexity compared to the conventional DFT based method. Compared to the recursive wavelet transform (RWT) method, it has a faster response time. It is shown that IRWT possesses an improvement over a wide range of decaying dc component, harmonic distortions, frequency deviation and sampling frequency compared to the previously proposed methods. This characteristic of IRWT makes it a good candidate for the real-time applications in any power systems.     

基于动态线性逼近的非线性系统预测控制

对于一类常见多重时滞非线性离散时间系统,提出了基于动态线性逼近的增量型简化递推预测模型,广义预测控制律、噪声估计器以及带有参数限定时域长度的参数自适应递推预报算法,实现了对存在较大滞后的时滞非线性系统的广义预测控制,通过仿真表明,该算法对于一类非线性系统实现预测控制是正确和有效的。     

采用最小累积风险度法的含风电场电力系统发电机组检修计划

以等风险度原则为基础,以各检修时段风险度方差最小为目标函数,建立了含风电场的电力系统发电机组检修计划模型,提出了基于最小累积风险度的启发式求解方法。采用半不变量描述负荷、发电机组出力等随机变量,将等效持续负荷曲线的卷积和反卷积运算转化为半不变量的加减。采用离散化的方法将风电机组等效成多状态机组,进而求取风电机组停运容量的半不变量。考虑风电场出力的不确定性,采用半不变量法结合革兰–沙利叶(Gram-Charlier)级数展开法求解系统的风险度。通过寻找检修区间内累积风险度最小的区间确定待检修机组的检修位置。IEEE-RTS系统的机组检修计划结果验证了该最小累积风险度法的有效性。     

A recursive technique for tracking the feasible parameter set in bounded error estimation

In this paper, a new recursive algorithm is proposed for tracking parameter changes of a time‐varying linear system. Since a bounded error approach is adopted for both modeling the measurement noise and the parameter change process, the problem addressed amounts to the design of a procedure for updating an estimate of the feasible parameter set. The approximating regions considered are in the form of outbounding orthotopes. The novelty of the approach lies in the use of a selection technique which keeps track only of a special subset of the constraints defining the feasible set. These inequalities represent the binding constraints of suitable linear programs of limited size. The devised algorithm is tested on several numerical examples, showing remarkable performance both in computational burden, which is comparable to that of classical recursive estimation algorithms like recursive least squares (RLS) and quality of the set estimate as compared to alternative techniques available in the literature.     

交流电参数的卷积窗加权测量及其单片机实现

分析了交流电电压、电流以及功率等均值参数的采样法测量理论,给出了不引入测量误差的条件。分析了非同步采样导致交流电参数测量误差的原因以及采用窗函数加权抑制测量误差的机理。重点给出了非同步频偏很小时抑制误差最有效的矩形互卷积窗及误差公式。针对实际中模数转换(ADC)电路的输入信号中存在直流分量,导出了适用于单片机直接实现交流电参数测量的卷积窗加权算法的系列公式。通过仿真实验测试了单片机执行卷积窗加权算法的机器周期数与时长。结果表明采用STM32单片机仅需利用采样周期的很小一部分时间即可完成2阶卷积窗加权算法交流电参数测量中1个采样点的计算,算法具有很高的时效性。     

基于长期递归卷积网络的无创血压测量

血压是能够反映人们身体健康的一个重要指标,随着高血压人群分布范围日益增大,连续血压的监测变得愈发重要。本文提出了基于长期递归卷积网络对光电容积脉搏波进行血压连续无创测量的方法。首先将利用光电容积法采集到的脉搏波信号归一化、阈值处理和特征提取,然后用长期递归卷积网络从脉搏波中计算出血压。实验表明,当光电容积脉搏波信号直接输入时,所提方法比长短期记忆网络的平均绝对误差和均方误差分别提升了56.00%和73.25%。将特征参数作为输入时,该实验比光电容积脉搏波信号直接输入时的平均绝对误差和均方误差提升了59.55%和87.41%,相比直接输入,特征参数输入的实验效果更好,实现了血压的精确测量。     

基于时序特征的图卷积协同过滤推荐算法

基于图卷积神经网络的协同过滤推荐算法框架是目前最先进的推荐算法框架,该框架在用户-项目交互嵌入向量的特征学习中并未关注交互发生的时序性,但实际情况中,用户-项目交互普遍具有明显的时序特征,且是影响推荐性能的重要因素。因此,本文提出一种基于时序特征的图卷积协同过滤推荐算法,重做多个数据集,保留数据集时序特征等原始信息,总结归纳数据集中用户-项目交互的历史时序信息,并对其进行参数化处理,作为重要特征输入到图卷积网络模型训练的高阶协同信号传递中。在三个公开的官方数据集Gowalla,Yelp和Amazon-book上设置多组消融性实验,采用公认的评价指标ndcg和recall对推荐算法性能进行评价,实验结果证明,在同样参数设置下,基于时序特征的图卷积协同过滤推荐算法性能超越了现有同类型图卷积协同过滤推荐算法,验证了时序特征对提升推荐效果的积极作用,提高了模型训练效率和预测命中率,更加高效地解决网络信息过载问题,满足了更高的应用需求。     

基于混合窗的改进全相位谐波相位检测算法

在实际谐波检测过程中,传统傅里叶算法只能做到非同步采样,这样就无法避免栏栅效应和频谱泄露的存在,导致谐波检测中相位检测精度相对较低。针对此问题,提出hanning窗和4项5阶nuttall窗的二阶混合卷积窗,用其在相位检测中做预处理,并以此提出基于混合卷积窗的改进全相位傅里叶算法,该算法在谐波相位检测中能有效提高谐波相位检测精度。分别在白噪声和频率波动的影响下做仿真验证,并与传统的加窗插值算法作对比分析。仿真结果表明,文中优化算法在谐波相位检测中具有较大优势,提高4个数量级的精确度,且具有较强抗白噪声和频率波动能力。     

Research on Fall Detection Based on Improved Human Posture Estimation Algorithm

According to recent research statistics, approximately 30% of people who experienced falls are over the age of 65. Therefore, it is meaningful research to detect it in time and take appropriate measures when falling behavior occurs. In this paper, a fall detection model based on improved human posture estimation algorithm is proposed. The improved human posture estimation algorithm is implemented on the basis of Openpose. An improved strategy based on depthwise separable convolution combined with HDC structure is proposed. The depthwise separable convolution is used to replace the convolution neural network structure, which makes the network lightweight and reduces the re-dundant layer in the network. At the same time, in order to ensure that the image features are not lost and ensure the accuracy of detecting human joint points, HDC structure is introduced. Experiments show that the improved algorithm with HDC structure has higher accuracy in joint point detection. Then, human posture estimation is applied to fall detection research, and fall event modeling is carried out through fall feature extraction. The designed convolution neural network model is used to classify and distinguish falls. The experimental results show that our method achieves 98.53%, 97.71% and 97.20% accuracy on three public fall detection data sets. Compared with the experimental results of other methods on the same data set, the model designed in this paper has a certain improvement in system accuracy. The sensitivity is also improved, which will reduce the error detection probability of the system. In addition, this paper also verifies the real-time performance of the model. Even if researchers are experimenting with low-level hardware, it can ensure a certain detection speed without too much delay.     

基于递归最小二乘的单通道混合信号时延估计算法

针对单通道混合信号的时延估计问题,提出了一种基于递归最小二乘(recursive least squares,RLS)的算法,将时延估计分为了基于循环统计量的粗估计和基于RLS的细估计过程,用二乘方的时间平均准则,对时延粗估计值进行迭代更新,完成整个估计过程.该算法结构简单,收敛速度快,估计精度提升明显,但较依赖于两路...