Please use this identifier to cite or link to this item:
Title: Robust state estimation for power systems via moving horizon strategy
Authors: Chen, Tengpeng
Keywords: Robust State Estimation
Issue Date: 2017
Source: Chen, T. (2017). Robust state estimation for power systems via moving horizon strategy. Sustainable Energy, Grids and Networks, 10, 46-54.
Series/Report no.: Sustainable Energy, Grids and Networks
Abstract: In this paper, I propose a re-weighted moving horizon estimation (RMHE) to improve the robustness for power systems. The RMHE reduces its sensitivity to the outliers by updating their error variances real-time and re-weighting their contributions adaptively for robust power system state estimation (PSSE). Compared with the common robust state estimators such as the Quadratic-Constant (QC), Quadratic-Linear (QL), Square-Root (SR), Multiple-Segment (MS) and Least Absolute Value (LAV) estimator, one advance of RMHE is that the RMHE incorporates the uncertainty of process model and the arrival cost term during the optimization process. Constraints on states are also taken into account. The influence of the outliers can be further mitigated. Simulations on the IEEE 14-bus system show that the RMHE can obtain estimated results with smaller errors even when the outliers are present.
ISSN: 2352-4677
DOI: 10.1016/j.segan.2017.02.005
Schools: School of Electrical and Electronic Engineering 
Rights: © 2017 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Sustainable Energy, Grids and Networks, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

Files in This Item:
File Description SizeFormat 
Robust State Estimation for Power Systems via Moving Horizon Strategy (accepted version).pdf479.01 kBAdobe PDFThumbnail

Citations 20

Updated on Jun 16, 2024

Web of ScienceTM
Citations 20

Updated on Oct 25, 2023

Page view(s)

Updated on Jun 18, 2024

Download(s) 50

Updated on Jun 18, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.