Title: Notice of Violation of IEEE Publication Principles:Kalman-filter algorithm and PMUs for state estimation of distribution networks
Abstract: Notice of Violation of IEEE Publication Principles<br><br> "Kalman-Filter Algorithm and PMUs for State Estimation of Distribution Networks"<br> by Faridoon Shabaninia, Mahdi Amini, Mohammad Vaziri, Mahyar Zarghami, and Suresh Vadhva<br> in the Proceedings of the 15th IEEE International Conference on Information Reuse and Integration (IEEE IRI 2014), August 2014, pp.868-873<br><br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.<br><br> This paper duplicates content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br> "State Estimation of Active Distribution Networks: Comparison Between WLS and Iterated Kalman-Filter Algorithm Integrating PMUs"<br> by Styliani Sarri, Mario Paolone, Rachid Cherkaoui, Alberto Borghetti, Fabio Napolitano, and Carlo Alberto Nucci<br> in the Proceedings of the 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe 2012), October 2012, pp.1-8<br><br> <br/> Availability of data from Phasor Measurement Units (PMUs), characterized by their high accuracy to measure node voltage phasors, allows a simplification of the State Estimation (SE) problems. In this paper Iterated Kaiman Filter (IKF) algorithm, as a new method, has been used for SE of a test Active Distributed Network (ADN) integrating PMU measurements. In order to validate the results, Weighted Least Squares (WLS) method, as a common way for SE problems, is simulated. In this case study, IEEE 13-bus test system is used with considering one Distributed Generation (DG). Simulation results show the proper performance of the IKF method.
Publication Year: 2014
Publication Date: 2014-08-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 2
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