Wind farm layout optimization using genetic algorithm and its application to Daegwallyeong wind farm

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dc.contributor.authorPark, Jeong Wooko
dc.contributor.authorAn, Bo Sungko
dc.contributor.authorLee, Yoon Seungko
dc.contributor.authorJung, Hyunsukko
dc.contributor.authorLee, Ikjinko
dc.date.accessioned2019-12-20T06:20:30Z-
dc.date.available2019-12-20T06:20:30Z-
dc.date.created2019-12-17-
dc.date.issued2019-12-
dc.identifier.citationJMST Advances, v.1, no.4, pp.249 - 257-
dc.identifier.issn2524-7905-
dc.identifier.urihttp://hdl.handle.net/10203/270011-
dc.description.abstractThis paper proposes a new wind farm layout optimization methodology based on a genetic algorithm by implementing a simulation model considering wake effect. This method consists of (1) batch optimization to efficiently obtain a rough wind farm layout for the maximum energy production in a large scale, and (2) post-optimization to obtain a refined layout to further improve the energy production in a small scale. The proposed two-step optimization enables to efficiently optimize wind farm layout and thus can be applicable to layout optimization of large-scale wind farms. A case study with the actual Daegwallyeong wind farm shows that wake loss is improved by 2.3% point after the proposed layout optimization which means about 2.5% more energy production compared with the existing layout.-
dc.languageEnglish-
dc.publisherSpringer-
dc.titleWind farm layout optimization using genetic algorithm and its application to Daegwallyeong wind farm-
dc.typeArticle-
dc.type.rimsART-
dc.citation.volume1-
dc.citation.issue4-
dc.citation.beginningpage249-
dc.citation.endingpage257-
dc.citation.publicationnameJMST Advances-
dc.contributor.localauthorLee, Ikjin-
dc.contributor.nonIdAuthorAn, Bo Sung-
dc.contributor.nonIdAuthorLee, Yoon Seung-
dc.contributor.nonIdAuthorJung, Hyunsuk-
dc.description.isOpenAccessN-
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ME-Journal Papers(저널논문)
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