This paper presents an enhanced multi-baseline phase unwrapping algorithm by combining an unscented Kalman filter with an enhanced joint phase gradient estimator based on the amended matrix pencil model, and an optima...This paper presents an enhanced multi-baseline phase unwrapping algorithm by combining an unscented Kalman filter with an enhanced joint phase gradient estimator based on the amended matrix pencil model, and an optimal path-following strategy based on phase quality estimate function. The enhanced joint phase gradient estimator can accurately and effectively extract the phase gradient information of wrapped pixels from noisy interferograms, which greatly increases the performances of the proposed method. The optimal path-following strategy ensures that the proposed algorithm simultaneously performs noise suppression and phase unwrapping along the pixels with high-reliance to the pixels with low-reliance. Accordingly, the proposed algorithm can be predicted to obtain better results, with respect to some other algorithms, as will be demonstrated by the results obtained from synthetic data.展开更多
Recursive state estimation methods have aroused substantial attraction among many researchers and in particular, the drives research fraternity has shown increased interest in recent years. State estimators that surro...Recursive state estimation methods have aroused substantial attraction among many researchers and in particular, the drives research fraternity has shown increased interest in recent years. State estimators that surrogate direct measurements play an integral part in the operation of modern a.c. drives. Their robustness and accuracy are very much decisive for the performance of the drive. In this paper, a comparative analysis of the three nonlinear filtering schemes to estimate the states of a three phase induction motor on the simulated model is presented. The efficacy of Ensemble Kalman Filter (EnKF) against the traditional Jacobian based Filter or Extended Kalman Filter (EKF) and almost forbidden, hitherto least-attempted Unscented Kalman Filter (UKF) is very much exemplified. Theoretical aspects and comparative simulation results are investigated comprehensively with respect to three different scenarios viz., step changes in load torque, speed reversal, and low speed operation. Also, “Monte Carlo Simulation” runs have been exploited very extensively to show the superior practical usefulness of EnKF, by which the minimum mean square error (MMSE), which is often used as the performance index, ostensibly gets mitigated very radically by the proposed approach. The results throw light on alleviating the intrinsic intricacies encountered in EKF in parlance with the observer theory.展开更多
针对当前电力系统中数据采集与监视控制系统(Supervisory Control And Date Acquisition,SCADA)和相量测量单元(Phasor Measurement Units,PMU)并存,以及不同规格的PMU之间不能实现完全同步的现状,引入了一种基于不完全同步PMU的电力系...针对当前电力系统中数据采集与监视控制系统(Supervisory Control And Date Acquisition,SCADA)和相量测量单元(Phasor Measurement Units,PMU)并存,以及不同规格的PMU之间不能实现完全同步的现状,引入了一种基于不完全同步PMU的电力系统动态状态估计模型,与SCADA组成混合量测系统,在此基础上提出了一种考虑相位失配量的线性动态状态估计算法.结合线性定常系统卡尔曼滤波原理,分别对相位失配量和系统状态进行滤波处理,实现对系统状态的估计.相比于传统方法,该算法的雅可比矩阵为常数阵,缩短了状态估计时间,减少了不必要的迭代.采用Matlab平台在IEEE 30节点系统上进行仿真实验,结果表明,在PMU不完全同步的情况下,提出的方法较传统的线性算法具有更好的性能,在很大程度上减小了状态估计产生的误差.展开更多
基金supported by the National Natural Science Foundation of China(4120147961261033+2 种基金61461011)the Guangxi Natural Science Foundation(2014GXNSFBA118273)the Dean Project of Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing(GXKL061503)
文摘This paper presents an enhanced multi-baseline phase unwrapping algorithm by combining an unscented Kalman filter with an enhanced joint phase gradient estimator based on the amended matrix pencil model, and an optimal path-following strategy based on phase quality estimate function. The enhanced joint phase gradient estimator can accurately and effectively extract the phase gradient information of wrapped pixels from noisy interferograms, which greatly increases the performances of the proposed method. The optimal path-following strategy ensures that the proposed algorithm simultaneously performs noise suppression and phase unwrapping along the pixels with high-reliance to the pixels with low-reliance. Accordingly, the proposed algorithm can be predicted to obtain better results, with respect to some other algorithms, as will be demonstrated by the results obtained from synthetic data.
文摘Recursive state estimation methods have aroused substantial attraction among many researchers and in particular, the drives research fraternity has shown increased interest in recent years. State estimators that surrogate direct measurements play an integral part in the operation of modern a.c. drives. Their robustness and accuracy are very much decisive for the performance of the drive. In this paper, a comparative analysis of the three nonlinear filtering schemes to estimate the states of a three phase induction motor on the simulated model is presented. The efficacy of Ensemble Kalman Filter (EnKF) against the traditional Jacobian based Filter or Extended Kalman Filter (EKF) and almost forbidden, hitherto least-attempted Unscented Kalman Filter (UKF) is very much exemplified. Theoretical aspects and comparative simulation results are investigated comprehensively with respect to three different scenarios viz., step changes in load torque, speed reversal, and low speed operation. Also, “Monte Carlo Simulation” runs have been exploited very extensively to show the superior practical usefulness of EnKF, by which the minimum mean square error (MMSE), which is often used as the performance index, ostensibly gets mitigated very radically by the proposed approach. The results throw light on alleviating the intrinsic intricacies encountered in EKF in parlance with the observer theory.
文摘针对当前电力系统中数据采集与监视控制系统(Supervisory Control And Date Acquisition,SCADA)和相量测量单元(Phasor Measurement Units,PMU)并存,以及不同规格的PMU之间不能实现完全同步的现状,引入了一种基于不完全同步PMU的电力系统动态状态估计模型,与SCADA组成混合量测系统,在此基础上提出了一种考虑相位失配量的线性动态状态估计算法.结合线性定常系统卡尔曼滤波原理,分别对相位失配量和系统状态进行滤波处理,实现对系统状态的估计.相比于传统方法,该算法的雅可比矩阵为常数阵,缩短了状态估计时间,减少了不必要的迭代.采用Matlab平台在IEEE 30节点系统上进行仿真实验,结果表明,在PMU不完全同步的情况下,提出的方法较传统的线性算法具有更好的性能,在很大程度上减小了状态估计产生的误差.
