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.     

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。     

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.     

供用电系统谐波滤波与无功功率补偿仿真软件

谐波治理的重要手段是采用各种形式的滤波器,开发实用的滤波器设计软件非常必要。介绍了所开发的供用电系统谐波滤波与无功功率补偿仿真软件的基本功能及特点:用工程文件方式保存工程设计基本参数、滤波方案及滤波器参数配置等;可在一个数据表中进行4种滤波器任意组合滤波方案的设置;仿真计算滤波电容器额定参数等,并可在额定频率和频率偏差情况下进行滤波效益与参数校验工作。所开发的软件参数输入与修改便捷,可方便地进行用户谐波电压与电流超标情况、电容器谐波放大问题分析、滤波效果校验、滤波器阻频特性分析等。在某钢厂的应用实例证明该软件可靠、稳定、方便。     

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.     

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     

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.     

A time-domain three-phase power system impedance modeling approachfor harmonic filter analysis

A time-domain method for impedance modeling of an unbalanced, three-phase power transmission and distribution network using three-phase measurements of bus voltage and injected current waveforms is presented. An impedance model seen at a bus where a harmonic filter is to be installed is calculated for each harmonic frequency, and the effect of placing the filter at the bus is then analyzed at these frequencies. As the configuration or operating condition of the power system varies, the estimated model changes accordingly, provided updated measurements are available. Hence, the effect of the harmonic filter under different system configurations or operating conditions is analyzed, and the information from this analysis is used for online harmonic filter control. Impedance models estimated by the proposed method are applied in filter design and performance analysis with actual recorded data, and the results are presented     

A fast method for identification of symmetrical components for power system protection

This paper presents a novel dynamic technique for identifying and measuring the symmetrical components of three-phase voltage or current waveforms in electrical power systems. The proposed technique is based on stochastic estimation theory. The problem is formulated as an estimation problem and presented in state space form. The algorithm is then used to estimate the active and reactive components of the positive, negative and zero components of unsymmetrical waveforms. The method is tested using practical study cases. Different factors affecting the identification process such as data window size, sampling frequency and number of samples are studied. Effects of bad data on the solution accuracy are also studied. The speed of convergence is examined by changing the estimator initial conditions. The 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 effect on the final accuracy of the estimation.     

基于双闭环模糊控制的有源电力滤波器

有源电力滤波器普遍采用双闭环控制.从双闭环协调控制的思路出发,通过对有源电力滤波器运行时内外环之间的相互耦合的研究,总结了双闭环PI参数协调控制的规律,并把总结的控制规律应用模糊控制的方式对有源电力滤波器进行控制.仿真结果和实验测试结果表明所述方法不仅使得直流母线电压在启动过程中的超调量很小,电压收敛速度快,而且发出的冲击电流很小,从而大大增强了系统运行的可靠性和直流母线电压的稳定性.仿真结果和实验测试结果表明,该方案能显著提高系统响应速度,并使注入电网的谐波电流达到IEEE519-1992的要求.     

基于双闭环模糊控制的有源电力滤波器

有源电力滤波器普遍采用双闭环控制。从双闭环协调控制的思路出发,通过对有源电力滤波器运行时内外环之间的相互耦合的研究,总结了双闭环PI参数协调控制的规律,并把总结的控制规律应用模糊控制的方式对有源电力滤波器进行控制。仿真结果和实验测试结果表明所述方法不仅使得直流母线电压在启动过程中的超调量很小,电压收敛速度快,而且发出的冲击电流很小,从而大大增强了系统运行的可靠性和直流母线电压的稳定性。仿真结果和实验测试结果表明,该方案能显著提高系统响应速度,并使注入电网的谐波电流达到IEEE519-1992的要求。     

基于自适应卡尔曼滤波残差分析的谐波检测

针对卡尔曼滤波在电力系统谐波检测精度不高的问题,提出了基于最大似然卡尔曼滤波残差分析的谐波检测方法。最大似然卡尔曼滤波通过使用最大似然自适应地优化误差协方差矩阵和初始条件参数,实现对谐波扰动幅值的预估与校正,克服了传统卡尔曼滤波精度不高和易发散的问题。同时,利用估计残差的奇异性可检测谐波干扰的起止点。仿真结果表明,该方法能准确检测出动静态谐波信号的幅值,其估计残差可以快速准确地识别出谐波干扰出现的起止时刻。     

补偿电压型谐波源的混合型APF研究

串联型APF适合于补偿电压型谐波源,但其变压器体积大、绝缘水平高,且控制增益的最优值选择困难。为补偿电压型谐波源,本文提出了一种由并联型APF和电感电容构成的混合型APF。其中电感电容串联在电网与谐波源之间并谐振于工频,实现了对基波电流的无衰减传播和对谐波电流的高阻抗隔离;进入电网的残余谐波电流以及由于参数波动引起的基波电流损失由并联型APF补偿。理论分析与仿真结果表明：该混合型APF的控制方式简单,经济性和补偿效果均好于串联型APF,应用前景广阔。     

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。     

Study on a novel approach to active power filter control using neural network-based harmonic identification scheme

This paper proposes an effective control scheme for three-phase three-wire (3P3W) active power filter using neural-based harmonic identification scheme. To achieve excellent steady state and dynamic response, the feedback control plus feed-forward control structures is utilized in the proposed control algorithms. The steady-state error minimization is achieved by the feedback loop, where the proportional integral regulators were adopted in d-axis and q-axis of the synchronous rotating reference frame synchronized with grid voltages by using the phase-locked loop. The adaptive linear combiners are utilized in the feed-forward loop, which serves the purpose of load disturbance rejection, and it significantly enhances dynamic performance of active power filter (APF). The modeling of the APF is based on the state-space modeling technique and the decoupled state-space model in synchronous d–q frame is also presented. Moreover, the mathematical formulation of the neural harmonic identification scheme and the selection of learning rate are discussed. It is found that the optimal learning rate is a compromise between the steady-state accuracy and the requirement of dynamic response. To verify the theoretical analysis, extensive simulation results are presented under both the balanced and unbalanced load conditions. The validity and effectiveness of the presented scheme is substantially confirmed by the simulation and experimental results, and it can be easily extended to applications of single-phase and three-phase four-wire APFs.     

电网谐波功率潮流方向与电力滤波器的应用

分析了电网谐波功率潮流方向对其安装的无源滤波器和有源滤波器的影响,证明了只有根据电网谐波功率潮流的方向和谐波源类型来确定所使用的滤波器及其工作方式才能达到预期的滤波效果。     

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.