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|Title:||Covariance analysis of LAV robust dynamic state estimation in power systems||Authors:||Sun, Lu
Ho, Weng Khuen
Ling, Keck Voon
Maciejowski, Jan M.
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2019||Source:||Sun, L., Chen, T., Ho, W. K., Ling, K. V., & Maciejowski, J. M. (2019). Covariance analysis of LAV robust dynamic state estimation in power systems. IEEE Systems Journal, 14(2) 2801-2812. doi:10.1109/JSYST.2019.2936595||Journal:||IEEE Systems Journal||Series/Report no.:||IEEE Systems Journal||Abstract:||In power system state estimation, the robust Least Absolute Value robust dynamic estimator is well-known. However, the covariance of the state estimation error cannot be obtained easily. In this paper, an analytical equation is derived using Influence Function approximation to analyze the covariance of the robust Least Absolute Value dynamic state estimator. The equation gives insights into the precision of the estimation and can be used to express the variances of the state estimates as functions of measurement noise variances, enabling the selection of sensors for specified estimator precision. Simulations on the IEEE 14-bus, 30-bus and 118-bus systems are given to illustrate the usefulness of the equation. Monte-Carlo experiments can also be used to determine the covariance, but many data points are needed and hence many runs are required to achieve convergence. Our result shows that to obtain the covariance of the state estimation error, the analytical equation proposed in this paper is four-order of magnitude faster than a 10,000-run Monte-Carlo experiment on both the IEEE 14-bus and 30-bus systems.||URI:||https://hdl.handle.net/10356/141874||ISSN:||1932-8184||DOI:||http://dx.doi.org/10.1109/JSYST.2019.2936595||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.2019.2936595||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
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