稳健性(进化)
控制理论(社会学)
估计员
卡尔曼滤波器
迭代函数
扩展卡尔曼滤波器
算法
数学
集合卡尔曼滤波器
计算机科学
统计
数学分析
生物化学
化学
控制(管理)
人工智能
基因
作者
Javad Enayati,Abolfazl Rahimnejad,Luigi Vanfretti,S. Andrew Gadsden,Mohammad Al-Shabi
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:72: 1-10
被引量:6
标识
DOI:10.1109/tim.2022.3218539
摘要
Active filters (AFs) are effective tools for mitigating the detrimental effects of harmonic components on the power systems. The performance of AFs is significantly dependent on designing an accurate and robust estimator which is responsible for providing reference harmonic values. In this article, a novel technique, called sliding innovation (SI) cubature filter, is proposed to estimate the harmonic parameters, i.e., magnitude and phase, in various operating conditions. The proposed method exploits the concept of sliding mode control in the formulation of the measurement update step in the Bayesian filtering framework to enhance the robustness of the estimator. Furthermore, the iterated version of the proposed algorithm, called iterative SI cubature filter, is presented to enhance the accuracy of the estimator. The proposed method keeps its robustness and accuracy in the noisy conditions under the fault occurrence as well as power system transients. The obtained results from both simulation and experimental setup confirm that the proposed estimator is more accurate and robust with a higher convergence speed compared to the well-known discrete Fourier transform (DFT), cubature Kalman filter (CKF), iterated extended Kalman filter (IEKF), and particle filter (PF).
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