大规模动态系统的分布式状态估计算法

doi:10.6040/j.issn.1672-3961.0.2016.095

山东大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (6): 62-68.doi: 10.6040/j.issn.1672-3961.0.2016.095

大规模动态系统的分布式状态估计算法

孙一冰¹,付敏跃^2,3*,王炳昌¹,张焕水¹

1.山东大学控制科学与工程学院, 山东济南 250061;2.纽卡斯尔大学电气工程与计算机科学学院, 澳大利亚新南威尔士州纽卡斯尔 2308;3.广东工业大学自动化学院, 广东广州 510006

收稿日期:2016-03-18 出版日期:2016-12-20 发布日期:2016-03-18
通讯作者: 付敏跃(1958— ),男,浙江台州人,教授,博士,主要研究方向为网络控制系统, 智能电网, 信号处理与通讯, 量化反馈控制. ;E-mail:minyue.fu@newcastle.edu.au E-mail:sun_yibing@126.com
作者简介:孙一冰(1987— ),男,山东威海人,博士研究生,主要研究方向为分布式状态估计. E-mail:sun_yibing@126.com
基金资助:
国家自然科学基金资助项目(61120106011,61573221,61403233);国家科技支撑计划资助项目(2014BAF07B03)

Distributed state estimation algorithm for large-scale dynamic systems

SUN Yibing¹, FU Minyue^2,3*, WANG Bingchang¹, ZHANG Huanshui¹

1. School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China;
2. School of Electrical Engineering and Computer Science, University of Newcastle, NSW 2308, Australia;
3. School of Automation, Guangdong University of Technology, Guangzhou 510006, Guangdong, China

Received:2016-03-18 Online:2016-12-20 Published:2016-03-18

摘要/Abstract

摘要： 主要研究离散时间大规模动态系统的分布式状态估计问题。首先,将系统划分为若干个子系统,基于区域内部量测信息和邻居传递的信息,各子系统利用该算法对本地状态进行估计,降低状态变量的维数、算法的计算复杂度和通信压力。该算法独立运行,并且平行运行该算法可以有效减少整体运行时间。通过减弱约束条件,利用数学归纳法证明由该算法得到的估计误差协方差和预测误差协方差矩阵正定。根据系统能观测性秩判据和不等式技巧,证明误差协方差矩阵有上界,并且上界是有界的,保证该算法在应用中的可行性。最后通过仿真研究,验证主要结论。

关键词: 状态估计, 电力系统, 分布式估计, 最大后验估计, 动态系统

Abstract: The problem of distributed state estimation over discrete-time large-scale dynamic systems was studied. The system was divided into some subsystem, and based on the local measurement and the information received from its neighbors, each subsystem utilized the proposed algorithm to estimate its local state, which reduced the dimension of the state vector, and enjoyed low computational complexity and communication load. This algorithm was run independently and in parallel to effectively reduce the overall execution time. By weakening the constraint condition, the mathematical induction was used to prove that the state estimation and prediction error covariance matrices obtained from this algorithm were positive definite. The rank criterion of system observability together with the inequality technique were utilized to prove that error covariance matrices had upper bounds and the upper bounds were also existence and bounded, which supported the feasibility of this algorithm in applications. At last, simulations of an example were provided to demonstrate the main results.

Key words: state estimation, power system, dynamic system, distributed state estimation, MAP estimation

中图分类号:

TM744

孙一冰,付敏跃,王炳昌,张焕水. 大规模动态系统的分布式状态估计算法[J]. 山东大学学报(工学版), 2016, 46(6): 62-68.

SUN Yibing, FU Minyue, WANG Bingchang, ZHANG Huanshui. Distributed state estimation algorithm for large-scale dynamic systems[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 62-68.

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大规模动态系统的分布式状态估计算法

Distributed state estimation algorithm for large-scale dynamic systems

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