主动配电网背景噪声对同步相量算法精度的影响

文章分析了某电动汽车充电站电压信号的背景噪声功率谱,建立了自回归滑动平均(ARMA)有色滤波模型。在稳态和幅相调制情况下,分析了不同类型背景噪声的相量计算精度。研究表明,配电网背景噪声信号具有时间相关性,其功率谱呈下凹状,对同步相量算法的影响比高斯白噪声和粉色噪声大。稳态情况下,总体最小二乘旋转不变(TLS-ESPRIT)算法抗背景噪声能力更强;动态情况下,卡尔曼(Kalman)滤波算法和离散傅里叶变换(DFT)算法抗背景噪声能力更强。     

基于动态负荷预测的配电网动态状态估计方法

随着相量测量单元(PMU)在配电网中的逐步部署,配电网动态状态估计逐渐成为智能配电网中的一个重要研究课题。为改进电力系统动态状态估计算法状态预测环节,通过负荷预测模型预测电力系统运行状况;将各节点负荷作为扩展状态变量,负荷预测结果作为新增量测,建立等式约束关系;在容积卡尔曼滤波(CKF)算法框架下,提出基于动态负荷预测的配电网动态状态估计方法。与直接对状态变量做线性外推的传统状态预测方法相比,所提算法更符合配电网动态特性变化规律。在IEEE119节点配电系统上的仿真分析,证明了算法的有效性。     

基于自适应扩展卡尔曼滤波的锂离子电池荷电状态估计CSCD

锂离子电池荷电状态(SOC)是电池管理系统(BMS)重要的参数之一,准确估计可以提高电池的使用寿命。然而在SOC估计过程中,会受到如测量设备的精度、噪声等外界因素的干扰,降低SOC的估计精度。为了提高SOC的估计精度,针对扩展卡尔曼滤波(EKF)算法易受噪声干扰,提出了以新息自适应扩展卡尔曼滤波来提高SOC的估计精度和稳定性。通过实验工况采集的数据,并与传统的EKF进行对比,估计误差可以控制在3%以内,验证了该模型的有效性。     

基于奇异值理论与神经网络的内燃机失火故障诊断研究

对多缸内燃机的气缸爆发噪声信号进行平稳小波分解,取部分分解系数组成包含矩阵。该包含矩阵加强了气缸爆发噪声信号在气缸爆发频率所在频段的信息。利用奇异值理论提取包含矩阵的奇异值作为气缸爆发噪声信号的特征。将奇异值作为改进的BP神经网络的输入对神经网络进行训练和故障识别。研究结果表明,该方法可有效地诊断和识别多缸内燃机失火故障。     

基于EMAF的新型电力系统电压畸变工况下并网逆变器快速锁相方法

为了提高新型电力系统电压畸变工况下电压信号同步相位的检测精度,提出了一种基于增强型滑动平均滤波(EMAF)算法的快速锁相算法.首先使用dq旋转坐标变换获得直流电压信号;然后利用EMAF算法滤除谐波、负序分量及噪声;最后,通过数学推导公式获得并网同步信息,实现并网逆变器的高性能控制.该算法无需复杂的锁相闭环回路,即可实现...     

基于加权多新息AEKF的锂电池SOC在线估算

为实现大功率锂离子电池荷电状态的实时准确估算,以三元锂离子电池为研究对象,提出了一种加权多新息理论与自适应扩展卡尔曼滤波相结合的算法.利用多个时刻的残差和卡尔曼增益对估计值进行校正,并根据所包含的信息量为每个残差配置不同的权重.通过对系统噪声协方差和误差协方差的实时更新,自适应地调节和修正当前估计值.为验证算法合理性,采用二阶RC等效电路模型来表征电池动态特性,并在不同工况下进行实验验证.实验结果表明,在DST和BBDST工况下的估算均方根误差分别为1.31％和1.23％,验证了所提出算法具有良好的精度和收敛性.加权多新息自适应扩展卡尔曼滤波算法为锂电池的精确状态估算和广泛应用提供了理论基础.     

基于自适应滑模观测器的荷电状态估算方法

锂电池荷电状态(State of Charge, SOC)的精确估算是电池管理系统的必要基础,但锂电池受充放电倍率大小、老化速度等影响,存在强非线性、时变性等特性,SOC难以直接测量,只能结合海量电压和电流数据通过算法估算。本文提出一种自适应滑模观测器算法(Attitude Sliding Mode Observer, ASMO)对SOC估算,通过梯度下降的算法实时更新滑模观测器矩阵增益,以提高算法的精度和鲁棒性。在测试工况下将拓展卡尔曼滤波算法、滑模观测器算法与改进算法比较,结果表明,相较于传统拓展卡尔曼滤波算法和滑模观测器算法,改进过的算法具有更高的精度和鲁棒性,误差可控制在1%以内,验证改进算法有效性。     

基于改进的CDKF锂电池SOC估计方法

准确估算荷电状态(SOC)可以为电池之间的均衡管理提供依据,延长锂电池组整体的使用寿命.针对中心差分卡尔曼滤波算法(CDKF)存在较大线性误差的问题,提出一种改进的CDKF算法.在原算法中引入迭代滤波思想,多次利用测量信息更新状态量估算值,使得观测信息不断迭代更新,基于LM优化方法不断修正协方差矩阵,有效减小了线性误差.首先基于二阶阻容(RC)电路单元模型,选择最小二乘参数辨识方法,辨识出模型阻容参数;然后进行HPPC实验,验证电池等效模型的准确性;最后分别在恒流放电和动态工况下应用改进后的CDKF算法对电池SOC和电压进行估计,并将估计结果与CDKF算法进行比较.两种工况下验证结果表明改进后的CDKF算法精度更高,SOC估计精度可提升1.16％,最大估计误差小于1.7％,算法收敛时间也比原算法短,改进后的CDKF算法在估计精度和鲁棒性方面均有所提升,更具有应用优势.     

基于极值优先过零检测法的相量测量算法

针对在测量同步电机变频启动过程暂态电压和电流时,被测信号频率测量不准确的问题,提出了基于极值优先过零检测法的相量测量算法,并利用理想信号模型对比分析了所提算法与传统DFT算法的性能。该算法利用输入数据的极值寻找信号的真实过零点,得到信号的实际频率,再通过真有效值的定义求取信号的有效值,具有易于实现、运算量较小、精度较高的优点;仿真结果表明所提方法在频率线性变化、幅值波动等条件下,具有良好的测量精度。研究结果为测量频率范围较大的信号相量提供借鉴。     

基于线性补偿的宽频相量测量方法及其测试

随着可再生能源发电和电网中电力电子设备的占比不断提高,电力系统的宽频振荡已成为制约可再生能源消纳的重要因素。为抑制高比例可再生能源电力系统的宽频振荡与满足保护需求,文章提出了一种宽频相量的测量方法,并研制了相应的装置。首先,该装置基于加窗的离散傅里叶变换算法和三峰插值算法,实现多路多模态电压、电流的宽频相量测量;然后,基于线性回归的方法补偿了高频振荡相量的幅值和相位测量结果,在保障动态响应速度的同时,有效提高了装置的测量精度;最后,基于测试仪和实时数字仿真平台测试验证了所研制装置的宽频相量测量性能,为保障新型电力系统安全稳定运行及可再生能源消纳提供了数据支撑。     

基于调制分量卡尔曼滤波的短时扰动检测

提出了一种基于调制分量的低阶卡尔曼滤波模型．实时检测电能质量短时扰动。该方法将三相电压信号的αβ变换结果分解为基波信号和调制信号．提取调制信号的两个特征分量作为新的状态变量,避免了单独设置各次谐波为状态变量,降低模型阶次并提高暂态响应速度。根据暂态时滤波误差发生突变的特征,提出误差比阈值法判断暂态过程的起止时间。通过自适应修正系统噪声方差阵Q．降低模型误差并有效抑制滤波发散现象。Matlab仿真结果表明所提方法比普通卡尔曼滤波和低通滤波器具有更高的稳态精度和更快的暂态响应速度。     

State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter

Lead-acid batteries are widely used in conventional internal-combustion-engined vehicles and in some electric vehicles. In order to improve the longevity, performance, reliability, density and economics of the batteries, a precise state-of-charge (SoC) estimation is required. The Kalman filter is one of the techniques used to determine the SoC. This filter assumes an a priori knowledge of the process and measurement noise covariance values. Estimation errors can be large or even divergent when incorrect a priori covariance values are utilized. These estimation errors can be reduced by using the adaptive Kalman filter, which adaptively modifies the covariance. In this study, an adaptive extended Kalman filter (AEKF) method is used to estimate the SoC. The AEKF can reduce the SoC estimation error, making it more reliable than using a priori process and measurement noise covariance values.     

State of charge estimation for lithium-ion battery based on an intelligent adaptive unscented Kalman filter

Adaptive unscented Kalman filter (AUKF) has been widely used for state of charge (SOC) estimation of lithium-ion battery. The noise covariance of the conventional AUKF method is updated based on the innovation covariance matrix (ICM), which is estimated using the error innovation sequence (EIS). However, the distribution of EIS changes due to the time-varying noise, load current dynamics and modelling error, which will lead to inaccurate ICM estimation. Therefore, an intelligent adaptive unscented Kalman filter (IAUKF) method is proposed to detect the distribution change of EIS. Then, the ICM is estimated based on the EIS after the distribution change. Results show that the IAUKF method can improve SOC estimation accuracy significantly. Compared with that of the AUKF method, the root mean squared error and the mean absolute error of SOC based on the IAUKF method decrease by 43.70% and 72.37% under random walk discharge condition, respectively. In addition, the computation time of the IAUKF method slightly increases by 6.27% compared with that of AUKF method. Finally, the effect of initial parameters on the SOC estimation accuracy was analysed. The results indicate that proper algorithm tuning, such as initial window length of EIS for ICM update and the threshold value, can further improve the SOC accuracy based on the proposed IAUKF method. The proposed IAUKF method also shows high robustness against initial measurement noise covariance.     

Online parameters identification and state of charge estimation for lithium‐ion batteries using improved adaptive dual unscented Kalman filter

State of charge (SOC) is a vital parameter which helps make full use of battery capacity and improve battery safety control. In this paper, an improved adaptive dual unscented Kalman filter (ADUKF) algorithm is adopted to realize co‐estimation of the battery model parameters and SOC. Notably, the covariance matching method that can adapt the system noise covariance and the measurement noise covariance is used to improve the estimation accuracy. Besides, singular value decomposition (SVD) is utilized to deal with the non‐positive error covariance matrix in both unscented Kalman filters, further enhancing the stability of estimation algorithm. Verification results under Dynamic Stress test and Federal Urban Driving Schedule test indicate that improved ADUKF can achieve more accurate SOC estimates with error band controlled within 2.8%, while that of traditional dual unscented Kalman filter (DUKF) can only be controlled within 5%. Moreover, robustness analysis is also conducted and the validation results present that the proposed algorithm can still provide precise SOC prediction results under some disturbances, such as erroneous initial SOC, inaccurate battery capacity, and various ambient temperatures.     

基于M估计的抗差Kalman滤波算法在某混凝土重力坝变形监测中的应用

为了解决标准Kalman滤波法不能很好处理大坝变形观测粗差与状态方程异常的问题,提出了采用基于M估计的抗差Kalman滤波算法,在最小二乘准则的基础上,通过调整观测值对状态估计的比例权重,可得到模型参数的稳健估计,给出了其滤波准则及递推公式,并根据预测残差调节增益矩阵的大小,尽可能地削弱监测噪声和动态噪声里粗差的影响,让系统处于比较稳定的状态。实例应用结果表明,该算法不仅可提高滤波精度,且能很好地控制观测异常和动态扰动异常对监测的影响。     

考虑数据时延的电力系统两阶段动态状态估计方法

由于电力系统中SCADA数据和PMU数据采样频率不同,使得这两种数据存在时延。首先提出基于变点重复检测的PMU最佳缓冲长度计算方法,将SCADA数据和PMU数据统一到同一时间尺度下,然后将无迹变换与指数权函数抗差估计算法相结合,针对历史多数据断面进一步提出了两阶段无迹卡尔曼滤波鲁棒动态状态估计方法。该方法在每一断面内,首先用无迹变换和两参数指数平滑预测后的预测值与SCADA数据结合进行第一阶段滤波,然后再将滤波所得估计值与PMU数据结合进行第二阶段滤波。通过两阶段滤波,能够显著增大滤波过程中的量测冗余度,并且有效降低在混合数据滤波过程中量测精度较低的SCADA量测对精度较高的PMU量测的影响。基于IEEE-39节点标准系统对本文所提方法进行仿真,仿真结果表明,本文所提方法能够有效结合PMU数据和SCADA数据对电力系统进行动态状态估计计算,且估计精度高,鲁棒性好。     

A comparison of extended Kalman filter,particle filter,and least squares localization methods for a high heat flux concentrated source

State estimation procedures using the extended Kalman filter, particle filter, and least squares are investigated for a transient heat transfer problem in which a high heat flux concentrated source is applied on one side of a thin plate and ultrasonic pulse time of flight is measured between spatially separated transducers on the opposite side of the plate. This work is an integral part of an effort to develop a system capable of locating the boundary layer transition region on a hypersonic vehicle aeroshell. Results from thermal conduction experiments involving one-way ultrasonic pulse time of flight measurements are presented. Comparisons of heating source localization measurement models are conducted where ultrasonic pulse time of flight readings provide the measurement update to the extended Kalman filter, particle filter, and least squares. Two different measurement models are compared: (1) directly using the one-way ultrasonic pulse time of flight as the measurement vector and (2) indirectly obtaining distance from the one-way ultrasonic pulse time of flight and then using these obtained distances as the measurement vector. For the direct model, the Jacobian required by the extended Kalman filter and least squares is obtained numerically using finite differences and a finite element forward conduction solution. For the indirect model, the derivatives with respect to the state variables are obtained in closed form. Heating source localization results and convergence behavior are compared for the three inverse methods and the two measurement models. The extended Kalman filter, least squares, and particle filter methods using the one-way ultrasonic pulse time of flight measurement model (direct model) produced similar results when considering accuracy of converged solution, ability to converge to the correct solution, and smoothness of convergence behavior. The results provide quantified justification for moving forward with development of an extended Kalman filter-based localization solution.     

Application of Noise Reduction Technique in the Inverse Analysis Using Kalman Filter

Abstract

The inverse techniques usually employ the sensor measurement to estimate the unknown quantities. Regardless of sensor accuracy, the measurements contain some degrees of uncertainty and error, inadvertently. Inasmuch as, the inverse problems are ill-conditioned in general term, the measurement errors cause instabilities, perturbations, and excursions in the solution procedure. To handle the noise difficulties, a novel approach is proposed in the current study. In this method, the measurement errors are filtered to alleviate the noise priori to utilization of inverse method. The Kalman filter is implemented to remove the noise from the original sensor readings. Thereafter, the Levenberg–Marquardt method is implemented to predict the unknown. To evaluate the accuracy and robustness of the developed approach, a high nonlinear test case containing moving boundary heat conduction problem is investigated. Comparing the obtained results illustrates the improvement of inverse solution procedure by employing the noise filtering technique.     

A normal-gamma-based adaptive dual unscented Kalman filter for battery parameters and state-of-charge estimation with heavy-tailed measurement noise

This study simultaneously considers the state-of-charge (SOC) estimation and model parameter identification of lithium-ion batteries with outliers in measurements. Conventional Kalman-type filters may degrade performance in this case since they assume Gaussian-distributed measurement noise. To improve the SOC estimation accuracy under this condition, a robust normal-gamma (NG)-based adaptive dual unscented Kalman filter (NG-ADUKF) is proposed. First, by modeling the joint distribution of the state and auxiliary variables of the measurement noise as the NG distribution, the unscented Kalman filter (UKF) is integrated with the NG filter to deal with the heavy-tailed measurement noise. Second, the online parameter identification and SOC estimation are realized simultaneously by alternatively using two NG-based adaptive UKFs. The performance of the proposed algorithm is validated by the New European Driving Cycle and Urban Dynamometer Driving Schedule tests. Experimental results show that the proposed NG-ADUKF algorithm has more accurate SOC estimations compared with the dual UKF (DUKF) and the variational Bayes-based adaptive DUKF (VB-ADUKF) in the case of mistuning and outliers. Moreover, the proposed method is more computationally efficient than VB-ADUKF.     

一种基于卡尔曼滤波的齿盘测速方法

针对水电机组齿盘测速中齿盘的加工精度很难保证检测与控制对转速测量精度要求,以及有效提高齿盘测速可靠性及可信度等实际问题,提出基于卡尔曼滤波理论的单传感器齿盘测速方法,该方法的应用,使齿盘测速能够满足水电机组测速精度的要求,并具有可靠性高、实时性强,安装方便的特点。