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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25290
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dc.contributor.authorRamirez Gonzalez, Miguel-
dc.contributor.authorSegundo Sevilla, Felix Rafael-
dc.contributor.authorKorba, Petr-
dc.contributor.authorCastellanos-Bustamante, Rafael-
dc.date.accessioned2022-07-08T12:39:26Z-
dc.date.available2022-07-08T12:39:26Z-
dc.date.issued2022-06-21-
dc.identifier.issn0378-7796de_CH
dc.identifier.issn1873-2046de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/25290-
dc.description.abstractStatic security assessment (SSA) is fundamental in electrical network analysis. However, the growing complexity and variability of grid’s operating conditions can make it tedious, slow, computationally intensive, and limited or impractical for on-line applications when traditional approaches are considered. Since this may hinder the emerging analytical duties of system operators, data-driven alternatives are required for faster and sophisticated decision-making. Although different machine learning algorithms (MLAs) could be applied, Convolutional Neural Networks (CNNs) are one of the most powerful models used in many advanced technological developments due to their remarkable capability to identify meaningful patterns in challenging and complex data sets. According to this, a CNN based approach for fast SSA of power systems with N-1 contingency is presented in this paper. To contribute to the automation of model building and tuning, a settings-free strategy to optimize a set of hyperparameters is adopted. Besides, permutation feature importance is considered to identify only a subset of key features and reduce the initial input space. To illustrate the application of the proposed approach, the simulation model of a practical grid in Mexico is used. The superior performance of the CNN alternative is demonstrated by comparing it with two popular MLAs.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofElectric Power Systems Researchde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectPower system security assessmentde_CH
dc.subjectConvolutional neural networksde_CH
dc.subjectHyperparameter optimizationde_CH
dc.subjectFeature importancede_CH
dc.subjectMachine learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titleConvolutional neural nets with hyperparameter optimization and feature importance for power system static security assessmentde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Energiesysteme und Fluid-Engineering (IEFE)de_CH
dc.identifier.doi10.1016/j.epsr.2022.108203de_CH
dc.identifier.doi10.21256/zhaw-25290-
zhaw.funding.euNode_CH
zhaw.issue108203de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume211de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf173628de_CH
zhaw.webfeedElektrische Energiesysteme und Smart Gridsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Ramirez Gonzalez, M., Segundo Sevilla, F. R., Korba, P., & Castellanos-Bustamante, R. (2022). Convolutional neural nets with hyperparameter optimization and feature importance for power system static security assessment. Electric Power Systems Research, 211(108203). https://doi.org/10.1016/j.epsr.2022.108203
Ramirez Gonzalez, M. et al. (2022) ‘Convolutional neural nets with hyperparameter optimization and feature importance for power system static security assessment’, Electric Power Systems Research, 211(108203). Available at: https://doi.org/10.1016/j.epsr.2022.108203.
M. Ramirez Gonzalez, F. R. Segundo Sevilla, P. Korba, and R. Castellanos-Bustamante, “Convolutional neural nets with hyperparameter optimization and feature importance for power system static security assessment,” Electric Power Systems Research, vol. 211, no. 108203, Jun. 2022, doi: 10.1016/j.epsr.2022.108203.
RAMIREZ GONZALEZ, Miguel, Felix Rafael SEGUNDO SEVILLA, Petr KORBA und Rafael CASTELLANOS-BUSTAMANTE, 2022. Convolutional neural nets with hyperparameter optimization and feature importance for power system static security assessment. Electric Power Systems Research. 21 Juni 2022. Bd. 211, Nr. 108203. DOI 10.1016/j.epsr.2022.108203
Ramirez Gonzalez, Miguel, Felix Rafael Segundo Sevilla, Petr Korba, and Rafael Castellanos-Bustamante. 2022. “Convolutional Neural Nets with Hyperparameter Optimization and Feature Importance for Power System Static Security Assessment.” Electric Power Systems Research 211 (108203). https://doi.org/10.1016/j.epsr.2022.108203.
Ramirez Gonzalez, Miguel, et al. “Convolutional Neural Nets with Hyperparameter Optimization and Feature Importance for Power System Static Security Assessment.” Electric Power Systems Research, vol. 211, no. 108203, June 2022, https://doi.org/10.1016/j.epsr.2022.108203.


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