基于自适应陷波器的电网频率估计方法

本文针对电网频率难以精确测量的问题,提出利用自适应陷波器结构的滤波功能,结合电网电压基波信号及其谐波信号的特性,以陷波器实时输出平方值作为陷波器参数的优化目标函数,根据随机优化理论推导出陷波器参数在线迭代算法,从而对与频率有关的陷波参数进行自适应调整,达到电网频率估计的目的。该方法结构简单,通过仿真分析和实验测试,检验了所提出的频率估计算法的收敛速度和逼近精度,表明基于自适应陷波器的电网频率估计方法,可以准确估计出电网频率。     

Frequency estimation by demodulation of two complex signals

This paper presents a method for frequency estimation in a power system by demodulation of two complex signals. In power system analysis, the αβ-transform is used to convert three phase quantities to a complex quantity where the real part is the in-phase component and the imaginary part is the quadrature component. This complex signal is demodulated with a known complex phasor rotating in opposite direction to the input. The advantage of this method is that the demodulation does not introduce a double frequency component. For signals with high signal to noise ratio, the filtering demand for the double frequency component can often limit the speed of the frequency estimator. Hence, the method can improve fast frequency estimation of signals with good noise properties. The method loses its benefits for noisy signals, where the filter design is governed by the demand to filter harmonics and white noise. The method has been previously published, but not explored to its potential. The paper presents four examples to illustrate the strengths and weaknesses of the method     

基于自适应无迹卡尔曼滤波的动力电池SOC估计

针对传统无迹卡尔曼滤波(unscented Kalman filter,UKF)算法估计电池SOC时,在未知的干扰噪声条件下滤波精度较低和稳定性较差等问题,基于等效的二阶RC电路模型,提出自适应无迹卡尔曼滤波(adaptive unscented Kalman filter,AUKF)算法.在模型参数辨识的基础上,构建...     

New digital filter for unbalance distorted current and voltage estimation in power systems

This paper presents a novel algorithm for identifying and measuring the symmetrical components of distorted three phase voltage or current waveforms in electrical power systems. The proposed algorithm is capable of estimating the symmetrical components as well as the harmonic contents of a given unbalanced distorted signal. The proposed technique is based on stochastic estimation theorem. The problem is formulated as an estimation problem and presented in state space form. The proposed algorithm used to estimate the positive, negative and zero components of unsymmetrical waveforms as well as the harmonic content of a given distorted signal. Application of the proposed algorithm has been conducted on various test cases. Among which a practical simulated power system has been implemented using EMTP. Various scenarios are carried out to simulate realistic situations of unsymmetrical waveforms. Effects of bad data on the solution accuracy are also studied. The speed of convergence is examined by changing the estimator initial conditions. Results obtained show that the proposed technique can estimate and track the symmetrical components of non-stationary three phase unbalance voltage or current waveforms in noisy environments. Fast accurate solutions are guaranteed regardless of the initial conditions. It is also shown that bad measurements have no effects on the final accuracy of the estimation.     

基于Kalman滤波的车用锌空电池组SOC估算

锌空电池因其较好的工作特性,是极具潜力的电动汽车用电池,而对锌空电池SOC的估算是车用电池管理系统研究的关键。在基于传统安时累积法估算基础上,以Thevenin电池模型结合Kalman滤波算法,提高对SOC的估算精度。通过基于SAE J227a工况的仿真与实验,验证了该算法能够较好地解决SOCi对SOC估算的影响因素,进而有效改进估算结果,在环境温度和空气流通性充分的情况下,达到小于4.5%的误差值。     

OFDM系统中基于卡尔曼滤波和FFT的信道估计算法

本文提出了一种OFDM系统中基于卡尔曼滤波和FFT的二维导频辅助信道估计算法.OFDM系统作为一个多载波传输系统,其信道估计需要在时间和频率两个方向同时进行.在这种新算法中,首先在时间方向上采用标量卡尔曼滤波器对导频处信道信息进行估计,然后在卡尔曼估计的基础上进行频率方向上的FFT插值,从而估计出全部的信道信息.在性能上这种新方法与基于维纳滤波器的信道估计算法接近,但实现更为简单,更适合应用于实时传输系统的信道估计.     

Kalman filter for identification of power system fuzzy harmonic components

The ability to estimate the harmonic components in a power system is necessary for delivering a high quality power to the end user. This paper proposes a new approach for identifying harmonic components in a power system based on treating the non-fundamental sinusoid voltage or current waveform as a fuzzy noise having a linear model. The parameters of this model are assumed to be fuzzy numbers with a membership function that has central and spread values. Kalman filter is used for identifying the center and spread of each coefficient. It is assumed that the distortion of the sinusoid waveform is due to noise and/or undesired sinusoidal components with different frequencies. Kalman filter filters the noise, the undesired components will be estimated as a spread in the membership functions of the coefficients. Numerical examples are presented to illustrate the effectiveness of this technique.     

基于卡尔曼滤波的电力系统短期负荷预测

介绍了卡尔曼滤波的算法,给出了一套递推计算公式,将此算法应用于短期负荷预测,并针对负荷预测本身的特点对算法进行了改进,用两种算法进行了实际的负荷预测计算,取得了比较准确的预测结果。     

永磁同步电动机系统参数和状态的推广卡尔曼滤波估计

永磁同步电动机系统具有力矩系数大、力矩波动小以及控制灵活等优点,在许多精密伺服控制系统中得到广泛应用。本文在得到系统模型的基础上,利用推广的卡尔曼滤波器对非线性的永磁同步电动机系统参数和状态进行了估计,仿真结果验证了估计方法在数学上的可行性。     

Classification of power system disturbances using linear Kalman filter and fuzzy-expert system

Identification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection. This paper presents a new approach for power system disturbances identification and classification. The concept of linear Kalman filter together with discrete wavelet transform (DWT) is used to extract two parameters; the amplitude and the slope from the captured voltage or current waveform. 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 two parameters are the inputs to fuzzy-expert system that uses some rules on these inputs to identify the class to which the waveform belongs. To prove the ability of the new approach for classifying power system disturbances, detailed digital simulation and experimental results involving various types of power quality events are presented. The results depict that the proposed technique has the ability to accurately identify and classify PQ disturbances.     

Several variants of Kalman Filter algorithm for power system harmonic estimation

This paper presents the maiden application of a variant of Kalman Filter algorithm known as Local Ensemble Transform based Kalman Filter (LET-KF) for power system harmonic estimation. The proposed algorithm is applied for estimating the harmonic parameters of a power signal containing harmonics, sub-harmonics, inter-harmonics in presence of white Gaussian noise. These algorithms are applied and tested for both stationary as well as dynamic signals containing harmonics. The LET-KF algorithm reported in this paper is compared with the earlier reported Kalman Filter based algorithms like Kalman Filter (KF) and Ensemble Kalman Filter (EnKF) algorithms for harmonic estimation. The proposed algorithm is found superior than the reported algorithm for its improved efficiency and accuracy in terms of simplicity and computational features, since there are less multiplicative operations, which reduces the rounding errors. It is also less expensive as it reduces the requirement of storing large matrices, such as the Kalman gain matrix used in other KF based methods. Practical validation is carried out with experimentation of the algorithms with the real time data obtained from a large paper industry. Comparison of the results obtained with KF, EnKF and LET-KF algorithms reveals that the proposed LET-KF algorithm is the best in terms of accuracy and computational efficiency for harmonic estimation.     

Frequency measurement of distorted signals using Fourier and zero crossing techniques

An approach to the design of a digital algorithm for network frequency estimation is proposed. The algorithm is derived by using the Fourier and zero crossing techniques. The Fourier method is used for digital filtering and the zero crossing technique is applied to the cosine or sine components of the original signal, which is usually corrupted by higher harmonics. The algorithm showed a very high level of robustness as well as a high measurement accuracy over a wide range of frequency changes. It can be used for frequency tracking in power networks when higher harmonics are present in the voltage or current signals. The theoretical basis and practical implementation of the technique are described. The performance of the developed algorithm has been verified by the computer simulations, and the field and laboratory tests.     

基于Kalman滤波原理的电力系统动态状态估计的研究综述

状态估计是现代EMS的重要组成部分,特别是动态状态估计,能实现实时运行状态的估计和预报功能。通过对动态估计算法Kalman滤波算法和国内外学者的一些改进算法的现状研究,分析了这些算法目前存在的主要问题。并基于此提出了Kalman滤波算法的新的改进措施,研究了方向和发展趋势。     

Modified multiplicative quaternion cubature Kalman filter for attitude estimation

In this paper, a modified multiplicative quaternion cubature Kalman filter (CKF) for attitude estimation is proposed. For high‐dimensional state estimation, the CKF that uses third‐degree spherical‐radial cubature rule can provide a more accurate estimation than the unscented Kalman filter. However, for the attitude estimation in the case of larger initial conditional errors, the results may be reversed. To take full advantage of the CKF, the Lagrange cost function method is introduced to solve the quaternion weighted mean, then, the mean is used as the reference quaternion for the measurement update in the CKF framework. The choice of the reference quaternions and the quaternion update method is different from the existing literature to avoid the algorithm from failing. In addition, the unconstrained three‐component vectors represent the attitude error quaternion in the filtering algorithm, whereas the quaternion is used to perform attitude propagation. Simulation results demonstrate the better performance of the proposed modifying filter algorithm in comparison with the multiplicative extended Kalman filter, the unscented Kalman filter, and the CKF under larger initial condition errors.     

A continuous-time least mean-phase adaptive filter for power system frequency estimation

The mean squared error criterion is widely used in the literature. However, there are applications where the squared error is not the primary parameter affecting the performance of a system. In this paper, we introduce a cost function that is based on the phase error. The criterion is useful for applications where the performance depends primarily on the phase of the estimated (recovered) signal. A continuous-time adaptive filter is then developed using the proposed criterion in stochastic differential equation formalism. The proposed adaptive filter is used 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. The performance of the new algorithm is studied through simulations at different situations of the power system.     

基于扩展卡尔曼滤波的LiFePO4电池荷电状态估计

为了准确估计电池的荷电状态(state-of-charge),在研究电池电化学阻抗谱模型的基础上得出了适合工程应用的电池简化等效电路模型,通过实验对模型进行了参数估计,并在模型的基础上采用扩展卡尔曼滤波法(EKF)对电池的SOC进行估计并使用Matlab进行了仿真验证,结果证明扩展卡尔曼滤波法能准确地估计电池的SOC。     

Exponential convergence of the Kalman filter based parameter estimation algorithm

In this paper we shall present a new method to analyse the convergence property of the Kalman filter based parameter estimation algorithms. This method for convergence analysis is mainly based on some matrix inequalities and is more simple than some of the existing approaches in the literature. This method can simultaneously provide both lower and upper bounds on the exponential convergence rate as the functions of bounds of the related matrices, such as the covariance matrices. A simulation example is provided to illustrate the convergence property of the Kalman filter based algorithms. Copyright © 2003 John Wiley & Sons, Ltd.     

Application of Kalman filtering for online estimation of symmetrical components for power system protection

This paper presents a new model that enables linear Kalman filtering (KF) to be applied for the direct estimation of symmetrical components in unbalanced three-phase systems. Computational experiments implementing KF using both synthetic and actual data sets are reported. It is shown that Kalman filtering models are well suited for symmetrical component estimation online. The effects of sampling rate, data window size and frequency drift on the optimal estimates are also studied.     

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.     

Decentralized unscented Kalman filter based on a consensus algorithm for multi-area dynamic state estimation in power systems

A decentralized unscented Kalman filter (UKF) method based on a consensus algorithm for multi-area power system dynamic state estimation is presented in this paper. The overall system is split into a certain number of non-overlapping areas. Firstly, each area executes its own dynamic state estimation based on local measurements by using the UKF. Next, the consensus algorithm is required to perform only local communications between neighboring areas to diffuse local state information. Finally, according to the global state information obtained by the consensus algorithm, the UKF is run again for each area. Its performance is compared with the distributed UKF without consensus algorithm on the IEEE 14-bus and 118-bus systems. The low communication requirements and high estimation accuracy of the decentralized UKF make it an alternative solution to the multi-area power system dynamic state estimation.