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  4. Publikationen School of Engineering
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dc.contributor.authorRamirez Gonzalez, Miguel-
dc.contributor.authorSegundo Sevilla, Felix Rafael-
dc.contributor.authorKorba, Petr-
dc.date.accessioned2021-08-19T12:23:49Z-
dc.date.available2021-08-19T12:23:49Z-
dc.date.issued2021-08-04-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/22971-
dc.description.abstractSteady-state response of the grid under a predefined set of credible contingencies is an important component of power system security assessment. With the growing complexity of electrical networks, fast and reliable methods and tools are required to effectively assist transmission grid operators in making decisions concerning system security procurement. In this regard, a Convolutional Neural Network (CNN) based approach to develop prediction models for static security assessment under N-1 contingency is investigated in this paper. The CNN model is trained and applied to classify the security status of a sample system according to given node voltage magnitudes, and active and reactive power injections at network buses. Considering a set of performance metrics, the superior performance of the CNN alternative is demonstrated by comparing the obtained results with a support vector machine classifier algorithm.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectPower system stabilityde_CH
dc.subjectStatic security assessmentde_CH
dc.subjectConvolutional neural networkde_CH
dc.subjectData-driven modelde_CH
dc.subjectDeep learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titleConvolutional neural network based approach for static security assessment of power systemsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Energiesysteme und Fluid-Engineering (IEFE)de_CH
zhaw.conference.detailsWorld Automation Congress 2021, virtual, 1-5 August 2021de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf173628de_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Ramirez Gonzalez, M., Segundo Sevilla, F. R., & Korba, P. (2021, August 4). Convolutional neural network based approach for static security assessment of power systems. World Automation Congress 2021, Virtual, 1-5 August 2021.
Ramirez Gonzalez, M., Segundo Sevilla, F.R. and Korba, P. (2021) ‘Convolutional neural network based approach for static security assessment of power systems’, in World Automation Congress 2021, virtual, 1-5 August 2021.
M. Ramirez Gonzalez, F. R. Segundo Sevilla, and P. Korba, “Convolutional neural network based approach for static security assessment of power systems,” in World Automation Congress 2021, virtual, 1-5 August 2021, Aug. 2021.
RAMIREZ GONZALEZ, Miguel, Felix Rafael SEGUNDO SEVILLA und Petr KORBA, 2021. Convolutional neural network based approach for static security assessment of power systems. In: World Automation Congress 2021, virtual, 1-5 August 2021. Conference paper. 4 August 2021
Ramirez Gonzalez, Miguel, Felix Rafael Segundo Sevilla, and Petr Korba. 2021. “Convolutional Neural Network Based Approach for Static Security Assessment of Power Systems.” Conference paper. In World Automation Congress 2021, Virtual, 1-5 August 2021.
Ramirez Gonzalez, Miguel, et al. “Convolutional Neural Network Based Approach for Static Security Assessment of Power Systems.” World Automation Congress 2021, Virtual, 1-5 August 2021, 2021.


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