Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137155
Title: Robust power system state estimation using t-distribution noise model
Authors: Chen, Tengpeng
Sun, Lu
Ling, Keck-Voon
Ho, Weng Khuen
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Chen, T., Sun, L., Ling, K.-V., & Ho, W. K. (2020). Robust power system state estimation using t-distribution noise model. IEEE Systems Journal, 14(1), 771-781. doi:10.1109/JSYST.2018.2890106
Journal: IEEE Systems Journal
Abstract: In this paper, we propose an optimal robust state estimator using maximum likelihood optimization with the $t$ -distribution noise model. In robust statistics literature, the $t$ -distribution is used to model Gaussian and non-Gaussian statistics. The influence function, an analytical tool in robust statistics, is employed to obtain the solution to the resulting maximum likelihood estimation optimization problem, so that the proposed estimator can be implemented within the framework of traditional robust estimators. Numerical results obtained from simulations of the IEEE 14-bus system, IEEE 118-bus system, and experiment on a microgrid demonstrated the effectiveness and robustness of the proposed estimator. The proposed estimator could suppress the influence of outliers with smaller average mean-squared errors (AMSE) than the traditional robust estimators, such as quadratic–linear, square-root, Schweppe–Huber generalized-M, multiple-segment, and least absolute value estimators. A new approximate AMSE formula is also derived for the proposed estimator to predict and evaluate its precision.
URI: https://hdl.handle.net/10356/137155
ISSN: 1932-8184
DOI: 10.1109/JSYST.2018.2890106
Schools: School of Electrical and Electronic Engineering 
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JSYST.2018.2890106.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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