Enhancing Transient Stability of Power Synchronization Control via Deep Learning

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorSepehr, Amiren_US
dc.contributor.authorPouresmaeil, Mobinaen_US
dc.contributor.authorPouresmaeil, Edrisen_US
dc.contributor.departmentRenewable Energies for Power Systemsen_US
dc.contributor.departmentDepartment of Electrical Engineering and Automationen_US
dc.date.accessioned2021-11-01T08:38:41Z
dc.date.available2021-11-01T08:38:41Z
dc.date.issued2021-10-25en_US
dc.description.abstractTransient stability of grid-connected converters has become a critical threat to the power systems with high integration level of renewable power generations. Thus, this paper aims to study the transient stability of power synchronization control (PSC) and propose a developed control system by employing deep learning methods. In order to extract and predict the voltage trajectory of the grid-connected converter system, a long short-term memory (LSTM) network has been trained and then integrated to PSC for adapting the synchronization loop of the converter to the grid condition. In the proposed control system, active power reference and internal voltage of the converter are updated dynamically to both satisfy the low voltage ride through (LVRT) requirements of the grid and prevent the loss of synchronization of the converter. The developed control system is validated by time-domain simulations.en
dc.description.versionPeer revieweden
dc.format.extent10
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationSepehr , A , Pouresmaeil , M & Pouresmaeil , E 2021 , Enhancing Transient Stability of Power Synchronization Control via Deep Learning . in Proceedings of the 23rd European Conference on Power Electronics and Applications, EPE’21 ECCE Europe . IEEE , European Conference on Power Electronics and Applications , Ghent , Belgium , 06/09/2021 . https://doi.org/10.23919/EPE21ECCEEurope50061.2021.9570417en
dc.identifier.doi10.23919/EPE21ECCEEurope50061.2021.9570417en_US
dc.identifier.isbn9781665433846
dc.identifier.isbn9789075815375
dc.identifier.otherPURE UUID: ecca539a-a89f-4f79-bebe-3d0592b78370en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/ecca539a-a89f-4f79-bebe-3d0592b78370en_US
dc.identifier.otherPURE LINK: https://ieeexplore.ieee.org/document/9570417en_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/67025997/ELEC_Sepher_etal_Enhancing_Transient_Stability_of_Power_Synchronization_IEEE_EPE_ECCE_2021_acceptedauthormanuscript.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/110759
dc.identifier.urnURN:NBN:fi:aalto-202111019934
dc.language.isoenen
dc.relation.ispartofEuropean Conference on Power Electronics and Applicationsen
dc.relation.ispartofseriesProceedings of the 23rd European Conference on Power Electronics and Applications, EPE’21 ECCE Europeen
dc.rightsopenAccessen
dc.titleEnhancing Transient Stability of Power Synchronization Control via Deep Learningen
dc.typeConference article in proceedingsfi
dc.type.versionacceptedVersion
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