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18824889.2021.1906017.pdf | 1.67 MB | Adobe PDF | 見る/開く |
完全メタデータレコード
DCフィールド | 値 | 言語 |
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dc.contributor.author | Ito, Kaito | en |
dc.contributor.author | Kashima, Kenji | en |
dc.contributor.author | Kato, Masakazu | en |
dc.contributor.author | Ohta, Yoshito | en |
dc.contributor.alternative | 伊藤, 海斗 | ja |
dc.contributor.alternative | 加嶋, 健司 | ja |
dc.contributor.alternative | 太田, 快人 | ja |
dc.date.accessioned | 2021-06-22T07:36:16Z | - |
dc.date.available | 2021-06-22T07:36:16Z | - |
dc.date.issued | 2021-04-25 | - |
dc.identifier.uri | http://hdl.handle.net/2433/263838 | - |
dc.description.abstract | Extreme outliers of wind power fluctuation are a source of severe damage to power systems. In our previous work, we proposed a modelling framework, verified its usefulness via real data, and developed a model-based evaluation method of the impact of such extreme outliers. However, it has been a drawback that the obtained estimates of frequency fluctuation of power systems are sometimes excessively conservative for their practical use. To overcome this weakness, theory and methods for tightening the fluctuation estimates are investigated in this paper. This is done by applying a robust performance analysis method of a Lur'e system to the error analysis of stochastic linearization. The usefulness of our proposed method is shown through a load frequency control model. | en |
dc.language.iso | eng | - |
dc.publisher | Informa UK Limited | en |
dc.rights | © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. | en |
dc.rights | This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | en |
dc.subject | Extreme events | en |
dc.subject | linearization | en |
dc.subject | renewable energy | en |
dc.subject | stochastic systems | en |
dc.subject | stable distribution | en |
dc.title | Stochastic model-based assessment of power systems subject to extreme wind power fluctuation | en |
dc.type | journal article | - |
dc.type.niitype | Journal Article | - |
dc.identifier.jtitle | SICE Journal of Control, Measurement, and System Integration | en |
dc.identifier.volume | 14 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 67 | - |
dc.identifier.epage | 77 | - |
dc.relation.doi | 10.1080/18824889.2021.1906017 | - |
dc.textversion | publisher | - |
dcterms.accessRights | open access | - |
datacite.awardNumber | 18H01461 | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-18H01461/ | - |
dc.identifier.pissn | 1882-4889 | - |
dc.identifier.eissn | 1884-9970 | - |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.awardTitle | 確率可制御性縮約による機械学習援用制御手法の可解釈性獲得 | ja |
jpcoar.funderName.alternative | Japan Society for the Promotion of Science (JSPS) | en |
出現コレクション: | 学術雑誌掲載論文等 |

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