Dynamic Event-Triggered State Estimation for Power Harmonics With Quantization Effects: A Zonotopic Set-Membership Approach

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Guhui Li;Zidong Wang;Xingzhen Bai;Zhongyi Zhao
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引用次数: 0

Abstract

This paper is concerned with the set-membership state estimation problem for power harmonics under quantization effects by using the dynamic event-triggered mechanism. The underlying system is subject to unknown but bounded noises that are confined to a sequence of zonotopes. The data transmissions are realized over a digital communication channel, where the measurement signals are quantized by a logarithmic-uniform quantizer before being transmitted from the sensors to the remote estimator. Moreover, a dynamic event-triggered mechanism is introduced to reduce the number of unnecessary data transmissions, thereby relieving the communication burden. The objective of this paper is to design a zonotopic set-membership estimator for power harmonics with guaranteed estimation performance in the simultaneous presence of: 1) unknown but bounded noises; 2) quantization effects; and 3) dynamic event-triggered executions. By resorting to the mathematical induction method, a unified set-membership estimation framework is established, within which a family of zonotopic sets is first derived that contains the estimation errors and, subsequently, the estimator gain matrices are designed by minimizing the $F$ -radii of these zonotopic sets. The effectiveness of the proposed estimation scheme is verified by a series of simulation experiments.
具有量化效应的电力谐波的动态事件触发状态估计:区位集合成员方法
本文利用动态事件触发机制,研究量化效应下的电力谐波集合成员状态估计问题。底层系统会受到未知但有界的噪声影响,这些噪声被限制在一连串的区位点上。数据传输是通过数字通信信道实现的,测量信号在从传感器传输到远程估计器之前由对数均匀量化器进行量化。此外,还引入了一种动态事件触发机制,以减少不必要的数据传输次数,从而减轻通信负担。本文的目的是设计一种用于电力谐波的区位集成员估计器,在同时存在以下情况时保证估计性能:1)未知但有界的噪声:1) 未知但有界的噪声;2) 量化效应;3) 动态事件触发执行。通过数学归纳法,建立了一个统一的集合隶属度估算框架,在此框架内,首先推导出包含估算误差的区opic集合族,然后通过最小化这些区opic集合的 $F$-radii 来设计估算器增益矩阵。一系列模拟实验验证了所提估计方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
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