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Robust Frequency and Phase Estimation for Three-Phase Power Systems Using a Bank of Kalman Filters
IEEE Signal Processing Letters ( IF 3.9 ) Pub Date : 2021-06-08 , DOI: 10.1109/lsp.2021.3087464
Zahraa Stuart , Yousef El-Laham , Monica Bugallo

In this paper we propose a powerful frequency, phase angle, and amplitude estimation solution for an unbalanced three-phase power system based on multiple model adaptive estimation. The proposed model utilizes the existence of a conditionally linear and Gaussian substructure in the power system states by marginalizing out the frequency component. This substructure can be effectively tracked by a bank of Kalman filters where each filter employs a different angular frequency value. Compared to other Bayesian filtering schemes for estimation in three-phase power systems, the proposed model reformulation is simpler, more robust, and more accurate as validated with numerical simulations on synthetic data.

中文翻译:

使用一组卡尔曼滤波器对三相电力系统进行稳健的频率和相位估计

在本文中,我们为基于多模型自适应估计的不平衡三相电力系统提出了一种强大的频率、相角和幅度估计解决方案。所提出的模型通过边缘化频率分量来利用电力系统状态中条件线性和高斯子结构的存在。该子结构可以由一组卡尔曼滤波器有效跟踪,其中每个滤波器采用不同的角频率值。与用于三相电力系统估计的其他贝叶斯滤波方案相比,所提出的模型重构更简单、更稳健且更准确,正如对合成数据的数值模拟所验证的那样。
更新日期:2021-06-25
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