1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25336
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRamirez Gonzalez, Miguel-
dc.contributor.authorSegundo Sevilla, Felix Rafael-
dc.contributor.authorKorba, Petr-
dc.date.accessioned2022-07-27T08:02:53Z-
dc.date.available2022-07-27T08:02:53Z-
dc.date.issued2022-
dc.identifier.isbn978-1-6654-6925-8de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/25336-
dc.description.abstractThe increasing penetration of non-synchronous generation into power grids is reducing the equivalent system inertia and leading to different frequency regulation and control challenges. Consequently, the monitoring and quantification of this inertia to implement actions that can keep it above critical levels have become a key issue for the stability of power systems. In this regard, a residual neural network (ResNet) based alternative is proposed and investigated in this paper to estimate the equivalent inertia of a sample system when synchronous generating units are displaced by converter-interfaced generators. The proposed ResNet model is trained according to the frequency of the center of inertia and the corresponding computed rates of change of frequency for a predefined time interval, where sudden generation outages and load step changes are considered under variations of total load demand and equivalent inertia reductions. The accuracy of the proposed approach is compared against the one achieved with the application of two traditional machine learning techniques, such as Support Vector Machine and Random Forest.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectInertia estimationde_CH
dc.subjectConvolutional neural networkde_CH
dc.subjectResidual neural networkde_CH
dc.subjectFrequency stabilityde_CH
dc.subjectConverter-interfaced generationde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titlePower system inertia estimation using a residual neural network based approachde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Energiesysteme und Fluid-Engineering (IEFE)de_CH
zhaw.publisher.placeNew Yorkde_CH
dc.identifier.doi10.1109/GPECOM55404.2022.9815784de_CH
dc.identifier.doi10.21256/zhaw-25336-
zhaw.conference.details4th Global Power, Energy and Communication Conference (GPECOM), Cappadocia, Turkey, 14-17 June 2022de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end360de_CH
zhaw.pages.start355de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of IEEE GEPCOM 2022de_CH
zhaw.webfeedElektrische Energiesysteme und Smart Gridsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2022_Ramirez-etal_Power-system-inertia-estimation_IEEE.pdfAccepted Version647.21 kBAdobe PDFThumbnail
View/Open
Show simple item record
Ramirez Gonzalez, M., Segundo Sevilla, F. R., & Korba, P. (2022). Power system inertia estimation using a residual neural network based approach [Conference paper]. Proceedings of IEEE GEPCOM 2022, 355–360. https://doi.org/10.1109/GPECOM55404.2022.9815784
Ramirez Gonzalez, M., Segundo Sevilla, F.R. and Korba, P. (2022) ‘Power system inertia estimation using a residual neural network based approach’, in Proceedings of IEEE GEPCOM 2022. New York: IEEE, pp. 355–360. Available at: https://doi.org/10.1109/GPECOM55404.2022.9815784.
M. Ramirez Gonzalez, F. R. Segundo Sevilla, and P. Korba, “Power system inertia estimation using a residual neural network based approach,” in Proceedings of IEEE GEPCOM 2022, 2022, pp. 355–360. doi: 10.1109/GPECOM55404.2022.9815784.
RAMIREZ GONZALEZ, Miguel, Felix Rafael SEGUNDO SEVILLA und Petr KORBA, 2022. Power system inertia estimation using a residual neural network based approach. In: Proceedings of IEEE GEPCOM 2022. Conference paper. New York: IEEE. 2022. S. 355–360. ISBN 978-1-6654-6925-8
Ramirez Gonzalez, Miguel, Felix Rafael Segundo Sevilla, and Petr Korba. 2022. “Power System Inertia Estimation Using a Residual Neural Network Based Approach.” Conference paper. In Proceedings of IEEE GEPCOM 2022, 355–60. New York: IEEE. https://doi.org/10.1109/GPECOM55404.2022.9815784.
Ramirez Gonzalez, Miguel, et al. “Power System Inertia Estimation Using a Residual Neural Network Based Approach.” Proceedings of IEEE GEPCOM 2022, IEEE, 2022, pp. 355–60, https://doi.org/10.1109/GPECOM55404.2022.9815784.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.