一种快速收敛的三相电信号参数估计几何统计方法

IF 11.7 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Luis Ibarra
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引用次数: 0

摘要

实时电信号的表征在电气工程应用中至关重要,例如同步和故障检测。然而,信号失真对参数估计提出了重大挑战,参数估计必须准确、快速。本文提出了一种新的电信号参数估计方法,该方法结合了一种新的几何统计框架,具有快速收敛和良好的精度。所提出的开环方法是基于递归协方差和椭圆表示的不平衡三相信号。它的数学公式促进了近似过程的实质性加速,最终达到了迄今为止最快的收敛时间。此外,所提出的方法在稳定时间方面始终优于其他估计方法,保持在基本信号的一个周期内。由此产生的频率或相位估计和TVE误差低于或与相关替代方案相当。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Fast Convergence Geometric–Statistical Approach for the Estimation of Three-Phase Electrical Signal Parameters
The characterization of real-time electrical signals is paramount in electrical engineering for applications, such as synchronization and fault detection. However, signal distortion poses significant challenges to parameter estimation, which must be accurate and fast. This study presents a novel approach to the estimation of electrical signal parameters that combines a new geometric–statistical framework with rapid convergence and good precision. The proposed open-loop approach is based on the recursive covariance and the elliptical representation of unbalanced three-phase signals. Its mathematical formulation facilitates a substantial acceleration of the approximation process, culminating in the fastest convergence time documented to date. In addition, the proposed method consistently outperforms other estimators in terms of settling time, remaining within one cycle of the fundamental signal. The resulting errors in frequency or phase estimation and TVE were lower than or comparable to those of relevant alternatives.
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来源期刊
IEEE Transactions on Industrial Informatics
IEEE Transactions on Industrial Informatics 工程技术-工程:工业
CiteScore
24.10
自引率
8.90%
发文量
1202
审稿时长
5.1 months
期刊介绍: The IEEE Transactions on Industrial Informatics is a multidisciplinary journal dedicated to publishing technical papers that connect theory with practical applications of informatics in industrial settings. It focuses on the utilization of information in intelligent, distributed, and agile industrial automation and control systems. The scope includes topics such as knowledge-based and AI-enhanced automation, intelligent computer control systems, flexible and collaborative manufacturing, industrial informatics in software-defined vehicles and robotics, computer vision, industrial cyber-physical and industrial IoT systems, real-time and networked embedded systems, security in industrial processes, industrial communications, systems interoperability, and human-machine interaction.
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