Kalman滤波新息正交性抗野值法研究

在Kalman滤波应用过程中，观测值中的野值是影响滤波效果的重要因素。当观测中含有野值时，破坏了Kalman滤波新息的正交性，从而造成估计不准，滤波精度下降。本文提出了修正Kalman滤波新息正交性的方法，使修正后的Kalman滤波新息能够保持修正前的新息正交性。仿真结果表明，本文提出的方法有效地抑制观测中的野值对系统滤波的不利影响。     

基于卡尔曼滤波的汽包水位多传感器信息融合方法研究

汽包水位是锅炉安全运行的重要参数,列举了影响汽包水位变化的各种因素并且建立了锅炉汽包系统各输入、输出变量间的影响模型。分析了卡尔曼滤波在多传感器信息融合处理中的特点,在DRZ/T01-2004规定的基础上,提出了一个以卡尔曼滤波为底层传感信号融合方法为基础,结合其他聚类融合方法,引入多种类、多数量传感器信号和控制决策预测信号的汽包水位多传感器数据融合控制系统。基于此,设计了卡尔曼滤波在多传感器数据融合处理中的具体实现方法,并借助Matlab仿真,分别测试了卡尔曼滤波在单通道传感信号滤波以及多传感器信息融合中使用的效果。仿真结果证明了所设计的系统能够准确、快速的融合处理底层传感器信号,并作出有效的控制决策。     

Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model

This paper presents a novel time-varying weather and load model for solving the short-term electric load-forecasting problem. The model utilizes moving window of current values of weather data as well as recent past history of load and weather data. The load forecasting is based on state space and Kalman filter approach. Time-varying state space model is used to model the load demand on hourly basis. Kalman filter is used recursively to estimate the optimal load forecast parameters for each hour of the day. The results indicate that the new forecasting model produces robust and accurate load forecasts compared to other approaches. Better results are obtained compared to other techniques published earlier in the literature.     

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。     

采用抗差扩展卡尔曼滤波器的感应电机转速估计方法

扩展卡尔曼滤波器(extended Kalman fileter,EKF)已广泛应用于无速度传感器矢量控制转速估计。尽管扩展卡尔曼滤波器具有较强的抗干扰能力,但是面对粗差时仍然会出现较大的抖动,影响系统的控制性能。提出了一种基于抗差扩展卡尔曼滤波器的转速估计方法,分析了粗差对扩展卡尔曼滤波器估算精度的影响,探讨了在应用于感应电机转速估计时抗差EKF能否同样取得良好的估计精度,以及优于EKF的抗粗差性能。通过仿真与实验,对比了遇到较大外部干扰和估算误差干扰时抗差EKF与EKF的转速误差和磁链变化。仿真与实验结果表明,抗差EKF较EKF而言具有更好的抗粗差性能,可以使系统遇到干扰时更快收敛。     

A Robust Extended Complex Kalman Filter and Sliding-mode Control Based Shunt Active Power Filter

Abstract—This article presents the design of a new shunt active power filter that employs a modified robust extended complex Kalman filter approach with an exponential robust term embedded for reference current estimation together with a current controller based on the sliding-mode control concept. The robust extended complex Kalman filter exploits a new weighted exponential function to handle these grid perturbations to estimate the reference signal in shunt active power filter system. The current controller in the proposed shunt active power filter has been designed using a sliding-mode control strategy because of its ability to handle parameter uncertainties and ease in implementation. To test the effectiveness of the proposed shunt active power filter, extensive simulations were performed using MATLAB/Simulink (The MathWorks, Natick, Massachusetts, USA), and real-time studies were made using OPAL-RT (Montreal, Quebec, Canada). Results obtained from the above studies using the proposed shunt active power filter together with the different variants of Kalman filter (Kalman filter, extended Kalman filter, extended complex Kalman filter) are analyzed, and it is observed that the proposed robust extended complex Kalman filter-sliding-mode control based shunt active power filter provides accurate and improved harmonics mitigation and reactive power compensation.     

基于WLS-KF的UWB室内定位滤波算法研究

针对室内超宽带(UWB)定位过程中受到非视距误差(NLOS)干扰而导致定位精度下降的问题,提出了基于抗差估计原理的自适应卡尔曼滤波方法,结合加权最小二乘法对测距信息解算得到定位坐标。在通视环境下进行测距,利用测得的数据计算新息向量和协方差,并基于此构建阈值信息,对NLOS环境产生的量测异常值进行判别,在此基础上利用Sage-Husa滤波对系统噪声协方差进行估计。采用加权最小二乘法对测距信息进行处理,得到标签解算坐标的最优估计。通过MATLAB仿真验证算法的可行性和有效性并在室内环境下进行测距、定位试验验证。仿真和实验结果表明,基于抗差估计原理的自适应卡尔曼滤波方法,结合加权最小二乘法能有效识别NLOS误差,且对定位过程中发生的状态突变能有效进行跟踪,解算得到的标签坐标x方向误差1 cm左右,y方向误差2 cm左右,提高了UWB室内定位的精度。     

永磁同步电机永磁体状况在线监测

提出一种基于卡尔曼滤波器的永磁同步电机永磁体磁场状况在线监测方法。通过选择磁场同步旋转坐标系下定子电流和永磁体磁链为状态变量，构建估算转子永磁体磁链幅值和方向的卡尔曼滤波器。该方法能准确跟踪永磁体真实状况，对电机参数不敏感，鲁棒性强。基于动态估算的电机永磁体磁链，可为永磁同步电机控制系统实时提供准确的转子磁链信息，提高系统控制性能和效率。同时，基于永磁体磁场状况的动态监测，可防止永磁电机失磁状况的恶化，降低不可逆失磁程度，提高系统可靠性。实验结果验证了方法的正确性和有效性。     

基于信号统计模型的变电站半遮挡融合定位方法

变电站存在建筑遮挡和电磁干扰等问题,这导致传统的基于电磁波定位的人员管控方法精度快速下滑。为避免因单传感器定位精度劣化而导致的电力安全管控效率降低问题,研究基于多源信息融合的巡检人员位置估计技术至关重要,而现有融合定位方法大多难以在地图信息未知的条件下鲁棒地选择传感器融合策略,因此文中提出一种基于卫星和近超声信号特征分析的融合定位方法,仅依靠信号统计特征实现环境信息判别并自适应选取融合策略。首先,利用多传感器信号特征统计模型构建指纹库,并基于t分布随机近邻嵌入(t-distributed stochastic neighbor embedding,t-SNE)降维算法和密度峰值聚类(density peaks clustering,DPC)算法处理指纹库数据。其次,依据聚类结果搭建反向传播(back propagation,BP)神经网络,将信号环境特征与卡尔曼滤波器的参数映射。最后,使用神经网络输出优化基于卡尔曼滤波的多源定位切换模型,形成自适应的融合定位方法。利用真实变电站半遮挡环境采集数据进行实验,结果表明,相较于未知环境信息、已知环境信息的融合定位方法,所提出的方法在地图信息未知的情况下节约了地图标定信息,实现了高鲁棒的位置估计。     

基于采样点卡尔曼滤波的动力电池SOC估计

动力电池荷电状态(SOC)的快速精确估计是电池能量管理系统的核心技术。针对动力电池这一动态非线性系统,提出了电池过程模型的具体改进方法,以使其可以适应不同放电速率和不同温度条件对动力电池SOC的影响;给出了利用采样点卡尔曼滤波进行电池SOC估计的具体步骤;最后,分析了采样点卡尔曼滤波在SOC估计精度、收敛速度、算法复杂度及鲁棒性等方面的性能。实验表明,采用采样点卡尔曼滤波算法可以快速地完成动力电池SOC的精确估计,误差在5%左右;模型参数的合理微调几乎不影响算法的准确性,表明了算法具有一定的鲁棒性。     

基于分布式两级控制的孤岛微网网络化控制策略

针对传统微网下垂控制的网络化控制策略中数据传输随机丢包对系统控制精度和稳定性影响的问题,提出一种基于分布式两级控制的孤岛微网网络化控制方法。采用初级控制实现负荷分配,同时增加分布式次级控制部分弥补电压和频率偏差提高孤岛微网负荷分配精度并维持微网稳定,通过改进的分布式卡尔曼(Kalman)滤波估计微网电压与频率输出状态,可有效避免数据传输随机丢包对系统稳定性的影响。所提的控制策略可实现二次型性能指标的全局最优控制,且对较小程度的数据丢包率具有鲁棒性。仿真实验验证表明,所給出的控制方法是有效可行的。     

Characterization of power quality disturbances using hybrid technique of linear Kalman filter and fuzzy-expert system

This paper presents a hybrid technique for characterizing power quality (PQ) disturbances. The hybrid technique is based on Kalman filter for extracting three parameters (amplitude, slope of amplitude, harmonic indication) from the captured distorted waveform. Discrete wavelet transform (DWT) is used to help Kalman filter to give a good performance; the captured distorted waveform is passed through the DWT to determine the noise inside it and the covariance of this noise is fed together with the captured voltage waveform to the Kalman filter. The three parameters are the inputs to fuzzy-expert system that uses some rules on these inputs to characterize the PQ events in the captured waveform. This hybrid technique can classify two simultaneous PQ events such as sag and harmonic or swell and harmonic. Several simulation and experimental data are used to validate the proposed technique. The results depict that the proposed technique has the ability to accurately identify and characterize PQ disturbances.     

基于自适应容积卡尔曼滤波算法的电力系统动态谐波状态估计

由于传统的谐波状态估计的参数辨识算法要求噪声的协方差矩阵固定不变,而实际工程中噪声的协方差矩阵是随时间变化的,工程中存在错误的量测数据,导致传统参数辨识算法估计的谐波电流参数的准确度较低。因此,提出自适应容积卡尔曼滤波算法来提高辨识谐波电流参数的准确度。首先,针对时变噪声干扰,采用基于渐消记忆指数加权法的噪声估值器算法生成时变噪声的协方差矩阵;其次,针对错误的量测数据,采用开窗估计算法修正错误的量测数据;然后,将修正的噪声协方差矩阵和量测数据代入容积卡尔曼滤波算法中,对谐波电流参数进行估计;最后,搭建IEEE 13节点系统仿真模型,验证了自适应容积卡尔曼滤波算法在时变噪声干扰及量测数据错误情况下仍可准确地估计谐波电流参数,确保了动态谐波状态估计的准确性。     

强噪声背景下的多精度传感器故障诊断

针对背景噪声变化很大、多精度冗余传感器故障难以诊断的问题,提出了基于二次卡尔曼滤波的故障诊断方法。该方法首先通过小波噪声估计预测观测值噪声强度,接着对传感器数据进行卡尔曼滤波预处理,降低观测值的不确定性,并将故障信息最大化,然后利用冗余特性,轮流使用一个传感器测量值作为输入,另一个作为输出建立循环卡尔曼滤波方程组,通过决策函数对所得到的新息进行故障诊断。实验分析了故障检测率与噪声强度的关系,结果表明,该方法能提高故障诊断的准确性,具有较好的鲁棒性。     

自适应抗野值Kalman滤波

针对Kalman滤波中存在量测野值的特点,依据滤波基本理论,提出了残差变化率概念.将残差变化率引入到野值判定标准中,根据量测值变化率的大小自动调整判断阀值,给出了判别野值的检验方法.由于采用了测量与估计的动态信息,使得野值的判定更为准确,并采用替代的方法对单个或连续野值加以修正,保证了Kalman滤波精确度与数据的连续性.仿真结果表明,当量测值出现较大变化时,这种方法能有效提高系统对野值的检测准确度和滤波精确度.     

基于改进鲁棒自适应UKF的配电网动态状态估计方法

配电网动态状态估计中状态方程的过程噪声统计参数是未知而且时变的,因此在状态估计过程中需要在线对过程噪声统计参数进行实时估计,而且不准确的噪声参数将会导致无迹卡尔曼滤波器的滤波性能下降甚至滤波发散。文中研究了基于改进鲁棒自适应无迹卡尔曼滤波器的配电网动态状态估计方法,其噪声参数统计估值器由一个有偏的和一个无偏的估值器组成,可以提高在状态估计过程中噪声参数估计的准确性,同时确保过程噪声方差矩阵的半正定性,从而保证算法的鲁棒性。通过对IEEE 33节点系统进行仿真验证,结果表明所提方法在系统平稳运行、负荷发生剧烈变动或者初始噪声参数值设置不当的情况下,均能保证较高的状态估计精度。     

Distributed Student's t filtering algorithm for heavy‐tailed noises

In this paper, a distributed Student's t filtering algorithm to deal with heavy‐tailed noises is developed. In the traditional Kalman filter, the distribution of the signal is assumed. However, in reality, outliers in the signal are often encountered for which the assumption of Gaussian distribution is no longer valid. The Student's t distribution can describe noises in the presence of outliers. As a result, the weight on each data point within the filter adapts to the data quality so that the filter becomes insensitive to the outliers. We first derive the distributed filtering algorithm from the centralized Student's t filter, which is able to handle heavy‐tailed noises such as outliers and then analyze properties of the proposed method. It is shown that the proposed algorithm provides the same accuracy as the centralized Student's t filtering with no performance loss. Furthermore, the distributed Student's t filtering with feedback is developed, which is in accordance with centralized filtering, and the local error covariance is reduced as expected. Two numerical examples support the theoretical results and illustrate the validity of the proposed method.     

A novel robust progressive cubature Kalman filter with variable step size for inertial navigationsystem/global navigation satellite system tightly coupled navigation

This article investigates the state estimation problem of the nonlinear system with the large process prior uncertainty but high-accuracy measurement information, the situation is prone to occur in the inertial navigation system (INS)/global navigation satellite system (GNSS) tightly coupled navigation system. Furthermore, the unknown heavy-tailed measurement noises induced by the inaccurate prior navigation information and random measurement outliers are also considered. Given existing methods such as progressive cubature Kalman filter (PCKF) cannot effectively solve the above issues, a novel robust PCKF with variable step size (RVSS-PCKF) is proposed. First, a new Gaussian-uniform-mixing inverse Gamma (GUMIG) distribution is presented to model the variable step size and measurement noise. Next, the GUMIG distribution is expressed as a hierarchical Gaussian presentation and then RVSS-PCKF is derived with the help of the variational Bayesian (VB) inference. In the filter, the state, variable step size and noise statistic are jointly estimated by the fixed-point iterations. Finally, the simulations and real data of the tightly coupled navigation illustrate the superiority of the filter in terms of accuracy and steady-state performance.     

基于MEEMD-KF-散布熵的油气管道工况识别

针对油气管道泄漏检测过程中,泄漏信号包含大量噪音、特征提取困难等问题,提出一种改进的总体平均经验模态分解联合卡尔曼滤波算法的管道信号去噪方法。首先采用改进的总体平均经验模态算法对采集到的管道负压波信号进行分解,其中利用排列熵和卡尔曼滤波算法对分解后的固有模态分量进行筛选和处理,最后得到重构后的削噪信号。并且提出基于散布熵和峭度的特征提取法,将提取的特征参数作为支持向量机的输入来对输油管道的工况进行分类识别。经采集到的数据验证,改进的总体平均经验模态分解、卡尔曼滤波、散布熵与峭度结合的组合识别方法可以较准确的对管道信号进行分类识别,结果显示其总平均识别准确率达到98.89%,为管道的工况识别研究提供了一种新的途径。     

Robust time‐varying Kalman estimators for systems with packet dropouts and uncertain‐variance multiplicative and linearly correlated additive white noises

This paper is concerned with robust estimation problem for a class of time‐varying networked systems with uncertain‐variance multiplicative and linearly correlated additive white noises, and packet dropouts. By augmented state method and fictitious noise technique, the original system is converted into one with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case system with conservative upper bounds of uncertain noise variance, the robust time‐varying Kalman estimators (filter, predictor, and smoother) are presented. A unified approach of designing the robust Kalman estimators is presented based on the robust Kalman predictor. Their robustness is proved by the Lyapunov equation approach in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Their accuracy relations are proved. The corresponding robust steady‐state Kalman estimators are also presented, and the convergence in a realization between the time‐varying and steady‐state robust Kalman estimators is proved. Finally, a simulation example applied to uninterruptible power system shows the correctness and effectiveness of the proposed results.