Distributed dynamic state estimation with parameter identification for large-scale systems. Issue 14 (September 2017)

• Record Type: Journal Article
• Authors: Sun, Yibing; Fu, Minyue; Wang, Bingchang; Zhang, Huanshui
• Is Part Of: Journal of the Franklin Institute. Volume 354:Issue 14(2017)
• Page Start: 6200
• Page End: 6216
• Publication Date: 2017-09
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2017.07.019
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: In this paper, we consider a distributed dynamic state estimation problem for time-varying systems. Based on the distributed maximum a posteriori (MAP) estimation algorithm proposed in our previous study, which studies the linear measurement models of each subsystem, and by weakening the constraint condition as that each time-varying subsystem is observable, this paper proves that the error covariances of state estimation and prediction obtained from the improved algorithm are respectively positive definite and have upper bounds, which verifies the feasibility of this algorithm. We also use new weighting functions and time-varying exponential smoothing method to ensure the robustness and improve the forecast accuracy of the distributed state estimation method. At last, an example is used to demonstrate the effectiveness of the proposed algorithm together with the parameter identification.