基于强跟踪滤波器的电力系统频率测量算法

频率在电力系统保护与控制、电能质量监测等领域都起到了关键作用。建立了谐波和噪声干扰下的电压信号的复数状态空间描述,提出了基于强跟踪滤波器(Strong Tracking Filter,STF)的电力系统频率测量算法。解决了扩展复数卡尔曼滤波(Extended Complex Kalmanfilter,ECKF)算法在算法收敛后,系统状态发生突变的情况下需要重置误差协方差阵来重新跟踪这些变化的问题,进一步提高了其动态跟踪速度。通过与鲁棒型扩展复数卡尔曼滤波(Robust Extended Complex Kalman Filter,RECKF)算法的对比仿真表明,STF测频算法在迅速跟踪电压频率、幅值和相位变化的同时又能够保持较低的跟踪误差。     

基于强跟踪滤波器的电力系统频率测量算法

频率在电力系统保护与控制、电能质量监测等领域都起到了关键作用。建立了谐波和噪声干扰下的电压信号的复数状态空间描述,提出了基于强跟踪滤波器(Strong Tracking Filter, STF)的电力系统频率测量算法。解决了扩展复数卡尔曼滤波(Extended Complex Kalman filter, ECKF)算法在算法收敛后,系统状态发生突变的情况下需要重置误差协方差阵来重新跟踪这些变化的问题,进一步提高了其动态跟踪速度。通过与鲁棒型扩展复数卡尔曼滤波(Robust Extended Complex Kalman Filter, RECKF)算法的对比仿真表明,STF测频算法在迅速跟踪电压频率、幅值和相位变化的同时又能够保持较低的跟踪误差。     

基于改进的RBAUKF的电力频率跟踪新算法

将电力系统的三相电压信号构造成一复信号,然后利用改进的RBAUKF算法对发生谐波和随机噪声干扰的电力系统电压信号进行动态估计和频率跟踪。针对电力系统中充满谐波畸变和随机噪声干扰,而复数型扩展卡尔曼滤波(ECKF)只是对非线性映射本身做某种线性近似等问题,在基于无迹卡尔曼滤波(UKF)算法的基础上,提出改进的无迹卡尔曼滤波(RBAUKF)的估计算法,并与复数型扩展卡尔曼滤波做了相应比较。经过对几种暂态电力系号模型的仿真试验表明,改进的RBAUKF在计算复杂度和估计精度上都优于ECKF。     

低频采样下基于卡尔曼滤波的同步相量测量算法的研究

各种基于定间隔采样的传统相量测量算法在跟踪速度和测量精度上不能很好地统一,在低频采样情况(每周波4~8个采样点)下提出一种基于线性卡尔曼滤波技术的10状态卡尔曼滤波模型,用于实时跟踪电力系统的电压有效值、频率、频率变化率和初相角,为系统提供精确的参数。结合二元泰勒展开公式,推导出各个参数的计算公式,并依据信号模型选取合适的时间区间保证算法对突变信号的响应速度。从采样频率、数据窗持续时间和算法对突变信号的跟踪精度方面对算法进行了讨论,结果表明使用该算法可以在低频采样下获得较高的参数估计精度。     

消除直流分量影响和谐波干扰的并网同步方法

在电网发生故障时,如何快速、准确的检测出电网同步信号,对逆变器电力电子控制至关重要。提出了一种基于多重复数滤波器(MCCF)与锁频环(FLL)的电网同步技术,复数滤波器分离出故障电网电压的正负序分量及谐波,锁频环进行电网锁相跟踪,并加入直流分量反馈环节。当电网故障时本文所提结构可以自动地在短时间内准确地跟踪电网电压频率,准确提取故障时电网电压正、负序分量和谐波成分,消除直流分量的影响,且具有较快的动态响应速度.确保并网电力电子器件的稳定、可靠运行。     

一种基于卡尔曼滤波的电能质量扰动检测新方法

基于正弦波/指数函数自相关的运动状态估计模型,提出一种基于卡尔曼滤波(Kalman filter,KF)的电能质量扰动(power quality disturbances,PQD)检测方法(KFPQD检测方法)。提出方法针对基波频率的纯正弦电压信号模型,以及基波频率纯正弦信号叠加指数衰减的电流信号模型,选取电压信号及其一阶导数形成二维电压状态向量,选取电流信号及其一阶导数、衰减直流分量形成三维电流状态向量,进行状态空间建模,离散化后采用KF算法对电压/电流及其变化率进行状态估计,跟踪检测基波电压/电流信号的跳变。仿真和微电网PQD检测实验结果表明:提出方法在跟踪精度、抗干扰性和敏感性方面均优于传统KFPQD方法,提高对电压与电流跳变的跟踪性能。     

基于自适应插值强跟踪扩展卡尔曼滤波的电力系统动态状态估计研究

针对扩展卡尔曼滤波(extended Kalman filter,EKF)算法在电力系统状态估计时存在鲁棒性差,精度被非线性系统的非线性程度制约大等缺点,提出一种自适应插值强跟踪扩展卡尔曼滤波(adaptive interpolation strong tracking extended Kalman filter,AISTEKF)算法,用于电力系统的动态状态估计。新算法利用自适应插值在两个连续采样点之间增加伪量测值,减小了EKF的线性化误差,有效提高了算法估计的精度;此外,该方法在EKF算法基础上引入强跟踪理论,增强了算法估计的鲁棒性。为验证所提出方法的有效性,分别运用EKF算法、自适应插值扩展卡尔曼滤波(adaptive interpolation extended Kalman filter,AIEKF)算法和AISTEKF算法对IEEE-5节点系统和IEEE-30节点系统进行动态状态估计。实验结果表明,与EKF和AIEKF算法相比,无论在高斯噪声环境下还是3种有偏噪声环境下,AISTEKF算法的电压幅值估计精度和电压相角估计精度都有显著性提高。所提出的新算法是一种鲁棒性好且估计...     

基于希尔波特-黄变换的介损数字测量算法

准确提取基波电压和电流信号是检测介质损耗因素的关键。提出了一种新的介质损耗因数检测算法,采用希尔波特-黄变换(HHT)对试品电压和电流信号进行检测,通过经验模态分解法(EMD)提取信号的固有模态函数(IMF),再进行Hilbert变换,得到各自的瞬时频率,由瞬时频率进行介质损耗因数的准确检测。该算法无需同步采样,可以实时提取测量电压、电流信号的基波成分。仿真结果表明,HHT受采样数据长度、频率跟踪误差的影响较小,在非同步采样的情况下,具有良好的应用特性,能有效提高介质损耗因数检测的准确度。     

电网瞬时频率的一种跟踪算法

基于双正交数字滤波器与加权平滑相位差分法,提出了一种跟踪电网瞬时频率的方法,改进了目前基于双正交数字滤波器的电网频率跟踪方法。针对电力信号的几种模型,详细仿真了跟踪算法的动态特性,指出针对恒频率和变频率电力信号的跟踪误差分别为0.000 1 Hz和0.005 Hz,并具体分析了影响跟踪误差和滞后效应的因素。研究表明,该方法不受同步采样限制,可较为准确、实时地跟踪各种电力信号的瞬时频率,其性能指标优于同类文献中的跟踪算法。     

基于容积卡尔曼滤波的多峰MPPT算法

针对局部阴影下光伏阵列的最大功率点(MPP)误跟踪问题,利用传统的扰动观察法确定MPP的电压范围,用较小步长控制电压变化,结合非线性容积卡尔曼滤波(CKF)算法估计各电压的功率大小,抑制观察法所带来的量测误差,同时根据状态估计的突变检测,快速寻找MPP,避免误跟踪情况的发生。通过具体实验分析,结果说明了该控制方案的精准性和有效性。     

强跟踪UKF算法在光伏系统MPPT中的应用

强跟踪UKF算法是采用Unscented策略逼近非线性分布且强跟踪系统突变的状态估计算法,该算法兼具强跟踪算法鲁棒性强、Unscented变换精度高、实现简单的优点。针对光伏系统在部分遮蔽情况下最大功率点误判问题,结合恒压法与强跟踪UKF算法,提出了一种新的光伏系统MPPT策略。采用恒压法快速定位最大功率点的电压范围;在该电压范围用尽量小步长的控制电压,以相应瞬时功率作为被估量,采用STUKF算法精确估算最大功率点,确定相应控制电压;保证光伏系统MPPT跟踪速度基础上,提高跟踪精度,通过状态跟踪判断状态突变,避免了误判问题。仿真和实验验证了所提策略的正确性、有效性。     

Dynamic Tracking of The Steady State Power System Magnitude and Frequency Using Linear Kalman Filter a Variable Frequency Model

ABSTRACT

This paper presents the application of Kalman Filtering algorithm for tracking the power system voltage magnitude, the rate of change of the frequency, the frequency deviation as well as the voltage phase angle, when the the frequency of the voltage signal varies linearly with the time during the data window size. The proposed algorithm uses the digitized samples of the voltage signal at the relay location. Effects of sampling rate, data window size and the harmonics contaminating the voltage signal on the performance of the algorithm are studied. Furthermore, effects of the noise level are also investigated. A sample of the results obtained is reported in this paper.     

基于改进卡尔曼滤波器的谐波检测方法

电力系统谐波问题日益严重且复杂,有效地对谐波进行检测已成为电能质量监测终端的一项基本要求.采用一种基于改进卡尔曼滤波器的谐波检测方法,利用卡尔曼滤波器在动态检测方面的优势,结合改进相位、频率的跟踪性能,可以简单、快速、准确的得到基波和谐波的各项参数.并且此方法可以检测到受闪变扰动时的基波和谐波成分,可获得闪变调制波的基本参数.建立了基于改进卡尔曼滤波器的谐波检测数学模型,并利用MATLAB仿真平台验证了该方法的性能.     

Frequency estimation of distorted power system signals usingextended complex Kalman filter

The paper proposes an extended complex Kalman filter and employs it for the estimation of power system frequency in the presence of random noise and distortions. From the discrete values of the 3-phase voltage signals of a power system, a complex voltage vector is formed using the well known αβ-transform. A nonlinear state space formulation is then obtained for this complex signal and an extended Kalman filtering approach is used to compute the true state of the model iteratively with significant noise and harmonic distortions. As the frequency is modeled as a state, the estimation of the state vector yields the unknown power system frequency. Several computer simulations test results are presented in the paper to highlight the usefulness of this approach in estimating near nominal and off-nominal power system frequencies     

Adaptive estimation of power system frequency deviation and itsrate of change for calculating sudden power system overloads

A novel Kalman filtering-based technique is presented for estimating power system frequency deviation and its average rate of change during emergency operating conditions that may require load shedding. This method obtains the optimal estimate of the power system frequency deviation from noisy voltage samples and the best estimate of the mean system frequency deviation and its rate of change while accounting for low-frequency synchronizing oscillations which occur during large disturbances. The proposed technique is a two-stage algorithm which uses an adaptive extended Kalman filter in series with an adaptive linear Kalman filter. The extended Kalman filter calculates the frequency deviation, magnitude, and phase angle of the voltage phasor, which may change during the time period covered by the estimation window. Both the measurement noise variance and the system noise covariance associated with the voltage samples are calculated online. The instantaneous frequency deviation is used as the input to a linear Kalman filter, which models the frequency deviation as a random walk plus a random ramp process. The estimated average rate of frequency decay is represented by the slope of the random ramp. Results for both single and multiple measurements are reported     

Improvement of Accuracy of Power System Spectral Analysis by Coherent Resampling

The coherent resampling (CR) algorithm, which improves the accuracy of discrete Fourier transform (DFT)-based power system frequency analysis, is described in the paper. The algorithm uses extended Kalman filter for instantaneous frequency tracking and fractional B-spline resampler for signal approximation. The CR algorithm is a software counterpart of synchronous sampling, i.e., sampling, synchronized with fundamental frequency of the signal. CR may either be applied to previously noncoherently sampled signals or to work in realtime. Frequency estimation, amplitude spectrum estimation and THD factor estimation results obtained by presented algorithm are compared to those obtained by other methods. Both simulated and real signals were used for tests. Brief summary of realtime DSP implementation of CR algorithm is also given.      

基于ASTUKF的电力信号同步相量跟踪算法

配电网电力信号的高噪声及强瞬变的特性对相量测量造成较大干扰,在噪声环境下可靠、快速并高精度地获得电力信号相量信息对电网的稳定性与可靠性具有重要意义。文章提出一种基于自适应强跟踪无迹卡尔曼滤波(Adaptive Strong Tracking Unscented Kalman Filter, ASTUKF)的电力信号同步相量跟踪算法：根据新息序列概率分布特征,构建系统状态判定变量判断系统是否发生突变,并在STUKF算法理论基础上提出弱化因子自适应算法,利用建立的基波相量状态空间模型构建基于ASTUKF的同步相量跟踪算法。实验结果表明,所提ASTUKF算法具有良好的检测突变的能力,能够快速准确地跟踪突变。     

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.     

基于卡尔曼滤波的北斗接收机高灵敏度跟踪算法研究

为了提升载波跟踪环路滤除噪声干扰的能力,引入一种作为可靠最优估计方法的卡尔曼滤波估计算法,用于解决高动态或弱信号环境载波跟踪环路难以稳定工作的问题.首先分析噪声对跟踪环路产生的影响,讨论采用卡尔曼滤波估计算法的理论设计依据,并且从环路结构、模型建立和优化设计三方面着手设计了自适应卡尔曼滤波跟踪环路.试验结果表明,无论在静态还是动态场景,其定位性能和精度与传统跟踪环路相比,均有较大幅度提升.