Improvement method of reducing reflectance in RAPSO-ME and its application to optimal allocation of SVCs

カテゴリ: 研究会(論文単位)

論文No: PE19064

グループ名: 【B】電力・エネルギー部門 電力技術研究会

発行日: 2019/09/10

タイトル(英語): Improvement method of reducing reflectance in RAPSO-ME and its application to optimal allocation of SVCs

著者名: Nishimura Junya(Okayama University),Takahashi Akiko(Okayama University),Imai Jun(Okayama University),Funabiki Shigeyuki(Okayama University)

著者名(英語): Junya Nishimura(Okayama University),Akiko Takahashi(Okayama University),Jun Imai(Okayama University),Shigeyuki Funabiki(Okayama University)

キーワード: Benchmark test|High-voltage distribution system|Particle swarm optimization|Photovoltaic generation|Static var compensator|Vector reflectance|Benchmark test|High-voltage distribution system|Particle swarm optimization|Photovoltaic generation|Static var compensator|Vector reflectance

要約(日本語): This paper proposes a method of reducing reflectance in a reflectance adjusting particle swarm optimization with mutation and elite group (RAPSO-ME) proposed by the authors. The reflectance is desired to be gradually reduced as increase of the number of iterations. The reflectance of the previous RAPSO-ME does not decrease due to the mutation even if the number of iterations increases. The proposed method focuses on SDs of initial evaluation value and best evaluation value of each particle gained in the past iterations. The availability of the proposed method is verified using benchmark tests. Furthermore, RAPSO-ME improved by the proposed method is applied to the optimal allocation of SVCs.

要約(英語): This paper proposes a method of reducing reflectance in a reflectance adjusting particle swarm optimization with mutation and elite group (RAPSO-ME) proposed by the authors. The reflectance is desired to be gradually reduced as increase of the number of iterations. The reflectance of the previous RAPSO-ME does not decrease due to the mutation even if the number of iterations increases. The proposed method focuses on SDs of initial evaluation value and best evaluation value of each particle gained in the past iterations. The availability of the proposed method is verified using benchmark tests. Furthermore, RAPSO-ME improved by the proposed method is applied to the optimal allocation of SVCs.

原稿種別: 英語

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