Publication:
A fast data-driven topology identification method for dynamic state estimation applications

dc.affiliation.dptoUC3M. Departamento de Ingeniería Eléctricaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Redes y Sistemas de Energía Eléctrica (REDES)es
dc.contributor.authorGotti, Davide
dc.contributor.authorLedesma Larrea, Pablo
dc.contributor.authorAmarís Duarte, Hortensia Elena
dc.contributor.funderAgencia Estatal de Investigación (España)es
dc.date.accessioned2022-12-20T09:47:11Z
dc.date.available2022-12-20T09:47:11Z
dc.date.issued2023-05
dc.description.abstractThis paper proposes a fast topology identification method to avoid estimation errors caused by network topology changes. The algorithm applies a deep neural network to determine the switching state of the branches that are relevant for the execution of a dynamic state estimator. The proposed technique only requires data from the phasor measurement units (PMUs) that are used by the dynamic state estimator. The proposed methodology is demonstrated working in conjunction with a frequency divider-based synchronous machine rotor speed estimator. A centralized and a decentralized approach are proposed using a modified version of the New England test system and the Institute of Electrical and Electronics Engineers (IEEE) 118-bus test system, respectively. The numerical results in both test systems show that the method demonstrate the reliability and the low computational burden of the proposed algorithm. The method achieves a satisfactory speed, the decentralized approach simplifies the training process and the algorithm proves to be robust in the face of wrong input data.en
dc.description.sponsorshipThis work was funded by Agencia Estatal de Investigación MCIN/AEI/10.13039/501100011033 under Grant PID2019-104449RB-I00.es
dc.format.extent13
dc.identifier.bibliographicCitationGotti, D., Ledesma, P., & Amaris, H. (2023). A fast data-driven topology identification method for dynamic state estimation applications. International Journal of Electrical Power & Energy Systems, 147(108807), 108807en
dc.identifier.doihttps://doi.org/10.1016/j.ijepes.2022.108807
dc.identifier.issn0142-0615
dc.identifier.publicationfirstpage1-108807
dc.identifier.publicationlastpage13-108807
dc.identifier.publicationtitleInternational Journal of Electrical Power & Energy Systemsen
dc.identifier.publicationvolume147
dc.identifier.urihttps://hdl.handle.net/10016/36204
dc.identifier.uxxiAR/0000031622
dc.language.isoeng
dc.publisherElsevieren
dc.relation.datasethttps://doi.org/10.21950/Z1E1QY
dc.relation.projectIDGobierno de España. PID2019-104449RB-I00es
dc.relation.projectIDAT-2022
dc.rights© 2022 The Author(s)en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaIngeniería Industriales
dc.subject.otherTopology Identificationen
dc.subject.otherDynamic State Estimationen
dc.subject.otherDeep Neural Networken
dc.subject.otherPhasor Measurement Unit (PMU)en
dc.subject.otherBad Data Detection and Identificationen
dc.titleA fast data-driven topology identification method for dynamic state estimation applicationsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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