Square Root Unscented Kalman Filter With Modified Measurement for Dynamic State Estimation of Power Systems
Square Root Unscented Kalman Filter With Modified Measurement for Dynamic State Estimation of Power Systems
复制标题
用于电力系统动态估计的修正测量平方根无迹卡尔曼滤波器
DOI：
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发表时间：
2022
影响因子：
5.6
通讯作者：
Badong Chen
中科院分区：
文献类型：
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作者：
Lujuan Dang;Wanli Wang;Badong Chen
电力系统的动态状态估计提供了有关其内在动态变化的基本信息。非线性卡尔曼滤波器（NKF）已被确定为潜在的通用工具，在执行状态估计。使用NKF的一个关键挑战在于，由于非高斯噪声的存在，降低了滤波精度和鲁棒性，因此可用的观测值是不准确的。通过鲁棒最优性准则或后验密度近似，设计了一些鲁棒NKF来校正状态估计。考虑到这些鲁棒NKF的本质是对误差协方差和噪声方差进行加权，本文提出了一种直接作用于测量值的加权方法。新方法是基于平方根无迹卡尔曼滤波器，可以提高数值稳定性。然后，将加权因子应用于测量模型以减轻异常测量误差的影响。加权因子是从考虑各向异性协方差函数和传统的“sinc”函数的上包络来获得的，用于抑制大的测量误差并保留非常大的误差的信息。我们称这种新的滤波器为修正量测的平方根无迹卡尔曼滤波器（SRUKF-MM）。在非高斯噪声环境下，对西部系统协调理事会（WSCC）3机系统和东北电力协调理事会（NPCC）48机系统进行了状态估计.仿真结果表明，该方法实现了显着改善滤波性能相比，其他相关的NKF。
Dynamic state estimation of a power system provides essential information about its inherent dynamic change. Nonlinear Kalman filters (NKFs) have been identified as potential versatile tools in performing state estimations. One key challenge of using NKFs lies in the fact that the available observations are inaccurate due to the presence of non-Gaussian noise that degrades the filtering precision and robustness. Some robust NKFs are developed to correct the state estimation via using robust optimality criteria or approximating the posterior density. Considering that the essence in these robust NKFs is to weight the error covariance and noise variance, we propose a novel method that can directly act on measurement to weight error covariance and noise variance. The novel method is based on a square root unscented Kalman filter that can enhance the numerical stability. Then, a weighting factor is applied to the measurement model for alleviating the effect of abnormal measurement errors. The weighting factor is derived from consideration of an anisotropy covariance function and an upper envelope of the traditional “sinc” function for suppressing large measurement errors and retaining the information of very large errors. We call the new filter the square root unscented Kalman filter with modified measurement (SRUKF-MM). The proposed SRUKF-MM is applied to state estimation of the Western System Coordinating Council (WSCC) 3-machine system and the Northeastern Power Coordinating Council (NPCC) 48-machine system in a non-Gaussian noise environment. Simulation results show that the proposed method achieves significantly improved filtering performance compared to other related NKFs.