1. Repositório Aberto da Universidade do Porto
  2. FEUP - Faculdade de Engenharia
  3. FEUP - Dissertação
Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/135306
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dc.creatorRodrigo Gonçalves de Morais
dc.date.accessioned2025-11-12T06:53:14Z-
dc.date.available2025-11-12T06:53:14Z-
dc.date.issued2021-07-19
dc.date.submitted2021-08-03
dc.identifier.othersigarra:486115
dc.identifier.urihttps://hdl.handle.net/10216/135306-
dc.descriptionMonte Carlo Simulation (MCS) is a powerful method frequently used for composite power system adequacy assessment. However it requires a considerable amount of time to provide accurate estimates for the reliability indexes. In the last years, mathematical approaches have been developed, for instance variance reduction techniques, with the aim to speed up this process. More recently, the MCS method has been implemented in parallel using a Graphics Processing Unit (GPU) to take advantage of the fast calculations provided by these computing platforms, resulting in reduction of the simulation time. In this dissertation, a new approach is developed to shrink simulation time by apllying Convolutional Neural Networks (CNN), trained on a GPU.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectEngenharia electrotécnica, electrónica e informática
dc.subjectElectrical engineering, Electronic engineering, Information engineering
dc.titleCONGRATS - Convolutional Networks in GPU-based Reliability Assessment of Transmission Systems
dc.typeDissertação
dc.contributor.uportoFaculdade de Engenharia
dc.identifier.doi10.34626/w9g9-2w10
dc.identifier.tid202825159
dc.subject.fosCiências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
dc.subject.fosEngineering and technology::Electrical engineering, Electronic engineering, Information engineering
thesis.degree.disciplineMestrado Integrado em Engenharia Electrotécnica e de Computadores
thesis.degree.grantorFaculdade de Engenharia
thesis.degree.grantorUniversidade do Porto
thesis.degree.level1
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