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A nested decision tree for event detection in smart grids

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Eléctricaes
dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Redes y Sistemas de Energía Eléctrica (REDES)es
dc.affiliation.grupoinvUC3M. Grupo de Investigación: COSEC (Computer SECurity Lab)es
dc.contributor.authorTuranzas, J.
dc.contributor.authorAlonso Martínez, Mónica
dc.contributor.authorAmarís Duarte, Hortensia Elena
dc.contributor.authorGutierrez, J.
dc.contributor.authorPastrana Portillo, Sergio
dc.date.accessioned2023-02-06T08:41:58Z
dc.date.available2023-02-06T08:41:58Z
dc.date.issued2022-09
dc.descriptionProcedings of: 20th International Conference on Renewable Energies and Power Quality (ICREPQ'22), 27-29 July 2022, Vigo, Spain.en
dc.description.abstractDigitalization process experienced by traditional power networks towards smart grids extend the challenges faced by power grid operators to the field of cybersecurity. False data injection attacks, one of the most common cyberattacks in smart grids, could lead the power grid to sabotage itself. In this paper, an event detection algorithm for cyberattack in smart grids is developed based on a decision tree. In order to find the most accurate algorithm, two different decision trees with two different goals have been trained: one classifies the status of the network, corresponding to an event, and the other will classify the location where the event is detected. To train the decision trees, a dataset made by co-simulating a power network and a communication network has been used. The decision trees are going to be compared in different settings by changing the division criteria, the dataset used to train them and the misclassification cost. After looking at their performance independently, the best way to combine them into a single algorithm is presented.en
dc.description.sponsorshipThis research was funded by Fundación Iberdrola España, within the 2020 research support scholarship program.en
dc.format.extent6
dc.identifier.bibliographicCitationTuranzas, J., Alonso, M., Amaris, H., Gutierrez, J. & Pastrana, S. (2022). A nested decision tree for event detection in smart grids. Renewable Energy and Power Quality Journal, 20, 353-358.en
dc.identifier.doihttps://doi.org/10.24084/repqj20.308
dc.identifier.isbn2172-038 X
dc.identifier.publicationfirstpage353
dc.identifier.publicationlastpage358
dc.identifier.publicationtitleRenewable Energy and Power Quality Journal (RE&PQJ)en
dc.identifier.publicationvolume20
dc.identifier.urihttps://hdl.handle.net/10016/36470
dc.identifier.uxxiCC/0000033873
dc.language.isoeng
dc.publisherEuropean Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ)en
dc.relation.eventdate2022-07-27e
dc.relation.eventplaceVigoen
dc.relation.eventtitle20th International Conference on Renewable Energies and Power Quality (ICREPQ'22)en
dc.rights© EA4EPQ, 2022.
dc.rights.accessRightsopen accessen
dc.subject.ecienciaElectrónicaes
dc.subject.ecienciaInformáticaes
dc.subject.otherCyberattacken
dc.subject.otherEvent detectionen
dc.subject.otherFDIen
dc.subject.otherMachine learningen
dc.subject.otherSmart Griden
dc.titleA nested decision tree for event detection in smart gridsen
dc.typeconference paper*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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