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  2. School of Engineering
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dc.contributor.authorRamirez Gonzalez, Miguel-
dc.contributor.authorSegundo Sevilla, Felix Rafael-
dc.contributor.authorKorba, Petr-
dc.contributor.authorCastellanos, Rafael-
dc.date.accessioned2024-07-12T09:34:38Z-
dc.date.available2024-07-12T09:34:38Z-
dc.date.issued2024-07-03-
dc.identifier.isbn979-8-3503-6161-2de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/31090-
dc.description.abstractAn approach based on Convolutional Neural Networks (CNNs) with a squeeze and excitation attention mechanism (SEAM) is investigated in this paper to assess the transient stability of a sample power system. In general, attention mechanisms in CNNs are intended to assist the selective focus of the model to increase their performance capabilities for different applications. In particular, the incorporation of the SEAM is considered here as a mean to automatically and explicitly model the importance of channel interdependencies in a given set of feature maps, which is accomplished by attention weights that modulate the influence of each channel. By collecting representative input-output examples from extensive simulations of the electric grid of Baja California Sur (BCS) in Mexico under different operational points, the response of the proposed approach to assess the transient stability of the grid is investigated on a sample dataset. Simulation results demonstrate that the CNN model with SEAM is able to provide enhanced learning performance and superior prediction response, as compared to the same CNN structure with no SEAM.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectPower system transient stabilityde_CH
dc.subjectConvolutional neural networkde_CH
dc.subjectSqueeze and excitation attention mechanismde_CH
dc.subjectFeature channel weightingde_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titleCNN with squeeze and excitation attention module for power system transient stability assessmentde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Energiesysteme und Fluid-Engineering (IEFE)de_CH
dc.identifier.doi10.1109/icSmartGrid61824.2024.10578176de_CH
zhaw.conference.details12th International Conference on Smart Grid (icSmartGrid), Setubal, Portugal, 27-29 May 2024de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedings2024 12th International Conference on Smart Grid (icSmartGrid)de_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, R. (2024, July 3). CNN with squeeze and excitation attention module for power system transient stability assessment. 2024 12th International Conference on Smart Grid (icSmartGrid). https://doi.org/10.1109/icSmartGrid61824.2024.10578176
Ramirez Gonzalez, M. et al. (2024) ‘CNN with squeeze and excitation attention module for power system transient stability assessment’, in 2024 12th International Conference on Smart Grid (icSmartGrid). IEEE. Available at: https://doi.org/10.1109/icSmartGrid61824.2024.10578176.
M. Ramirez Gonzalez, F. R. Segundo Sevilla, P. Korba, and R. Castellanos, “CNN with squeeze and excitation attention module for power system transient stability assessment,” in 2024 12th International Conference on Smart Grid (icSmartGrid), Jul. 2024. doi: 10.1109/icSmartGrid61824.2024.10578176.
RAMIREZ GONZALEZ, Miguel, Felix Rafael SEGUNDO SEVILLA, Petr KORBA und Rafael CASTELLANOS, 2024. CNN with squeeze and excitation attention module for power system transient stability assessment. In: 2024 12th International Conference on Smart Grid (icSmartGrid). Conference paper. IEEE. 3 Juli 2024. ISBN 979-8-3503-6161-2
Ramirez Gonzalez, Miguel, Felix Rafael Segundo Sevilla, Petr Korba, and Rafael Castellanos. 2024. “CNN with Squeeze and Excitation Attention Module for Power System Transient Stability Assessment.” Conference paper. In 2024 12th International Conference on Smart Grid (icSmartGrid). IEEE. https://doi.org/10.1109/icSmartGrid61824.2024.10578176.
Ramirez Gonzalez, Miguel, et al. “CNN with Squeeze and Excitation Attention Module for Power System Transient Stability Assessment.” 2024 12th International Conference on Smart Grid (icSmartGrid), IEEE, 2024, https://doi.org/10.1109/icSmartGrid61824.2024.10578176.


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