Efficient Design Method for a Forward-converter transformer based on a KNN–GRU–DNN Model
DC Field | Value | Language |
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dc.contributor.author | Lee, Gang Seok | - |
dc.contributor.author | Kim, Sanha | - |
dc.contributor.author | Bae, Sung Woo | - |
dc.date.accessioned | 2023-05-03T13:30:02Z | - |
dc.date.available | 2023-05-03T13:30:02Z | - |
dc.date.created | 2022-10-06 | - |
dc.date.issued | 2023-01 | - |
dc.identifier.issn | 0885-8993 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185356 | - |
dc.description.abstract | This letter proposes an efficient design method for a forward-converter transformer (FCT) with artificial intelligence (AI). Conventional FCT design is inefficient because it requires numerous repeated design processes. To solve this problem, this letter proposes FCT design by applying a KNN–GRU–DNN model. The design estimation accuracy of the proposed AI model was over 91% based on Google colaboratory validation. The proposed transformer design also satisfied the design requirements with less than 1,450 epochs. Once the learning process is completed, the proposed AI-based transformer design can obtain various FCT designs without further repeated training procedures. To verify the proposed design results, this study conducted finite-element method (FEM) simulations using ANSYS Electronics Desktop 2018.2 and hardware-in-the-loop (HIL) experiments using OPAL-RT with the transformer design values resulting from the AI-based design model. According to the FEM simulations and HIL experiments, it is verified that the secondary winding induced voltage of the transformer designed by the AI-based model satisfies the design requirements. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Efficient Design Method for a Forward-converter transformer based on a KNN–GRU–DNN Model | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Bae, Sung Woo | - |
dc.identifier.doi | 10.1109/TPEL.2022.3203480 | - |
dc.identifier.scopusid | 2-s2.0-85137938345 | - |
dc.identifier.wosid | 000864285600017 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON POWER ELECTRONICS, v.38, no.1, pp.73 - 78 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON POWER ELECTRONICS | - |
dc.citation.title | IEEE TRANSACTIONS ON POWER ELECTRONICS | - |
dc.citation.volume | 38 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 73 | - |
dc.citation.endPage | 78 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | KNN | - |
dc.subject.keywordPlus | FE | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | deep neural network (DNN) | - |
dc.subject.keywordAuthor | forward-converter transformer (FCT) | - |
dc.subject.keywordAuthor | gate-recurrent unit (GRU) | - |
dc.subject.keywordAuthor | K-nearest neighbors (KNN) | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9873977 | - |
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