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A Deep Neural Network Approach for Online Topology Identification in State Estimation

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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.authorAmarís Duarte, Hortensia Elena
dc.contributor.authorLedesma Larrea, Pablo
dc.date.accessioned2022-05-27T12:13:43Z
dc.date.available2022-05-27T12:13:43Z
dc.date.issued2021-11-01
dc.description.abstractThis paper introduces a network topology identification (TI) method based on deep neural networks (DNNs) for online applications. The proposed TI DNN utilizes the set of measurements used for state estimation to predict the actual network topology and offers low computational times along with high accuracy under a wide variety of testing scenarios. The training process of the TI DNN is duly discussed, and several deep learning heuristics that may be useful for similar implementations are provided. Simulations on the IEEE 14-bus and IEEE 39-bus test systems are reported to demonstrate the effectiveness and the small computational cost of the proposed methodology.en
dc.description.sponsorshipThis work was supported by the Spanish Ministry of Innovation under Grant PID2019-104449RB-I00. Paper no. TPWRS-01989-2020.en
dc.description.statusPublicadoes
dc.format.extent9
dc.identifier.bibliographicCitationIEEE Transactions on Power Systems, (2021), 36(6), pp.: 5824 - 5833.en
dc.identifier.doihttps://doi.org/10.1109/TPWRS.2021.3076671
dc.identifier.issn0885-8950
dc.identifier.publicationfirstpage5824
dc.identifier.publicationissue6
dc.identifier.publicationlastpage5833
dc.identifier.publicationtitleIEEE TRANSACTIONS ON POWER SYSTEMSen
dc.identifier.publicationvolume36
dc.identifier.urihttps://hdl.handle.net/10016/34917
dc.identifier.uxxiAR/0000029242
dc.language.isoengen
dc.publisherIEEEen
dc.relation.projectIDGobierno de España. PID2019-104449RB-I00es
dc.rights© 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaIngeniería Industriales
dc.subject.otherTopology identificationen
dc.subject.otherDeep neural networken
dc.subject.otherState estimationen
dc.subject.otherBad data detection and identificationen
dc.titleA Deep Neural Network Approach for Online Topology Identification in State Estimationen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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