dc.contributor.authorChen, Tengpeng
dc.date.accessioned2017-07-21T03:45:39Z
dc.date.available2017-07-21T03:45:39Z
dc.date.issued2017
dc.identifier.citationChen, T. (2017). Robust state estimation for power systems via moving horizon strategy. Sustainable Energy, Grids and Networks, 10, 46-54.en_US
dc.identifier.issn2352-4677en_US
dc.identifier.urihttp://hdl.handle.net/10220/42960
dc.description.abstractIn 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.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.format.extent22 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesSustainable Energy, Grids and Networksen_US
dc.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: [http://dx.doi.org/10.1016/j.segan.2017.02.005].en_US
dc.subjectRobust State Estimationen_US
dc.titleRobust state estimation for power systems via moving horizon strategyen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.segan.2017.02.005
dc.description.versionAccepted versionen_US


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