Optimal power flow using self-learning cuckoo search algorithm

Khai Phuc Nguyen, Goro Fujita

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

This paper proposes an improved Cuckoo search algorithm to solve optimal power flow problems in electric power system. The proposed Self-learning Cuckoo search algorithm enhances the performance of Cuckoo eggs by employing the learner phase of Teaching-learning-based optimization. The learner phase leads Cuckoo eggs to follow better solutions. The proposed method has been applied for solving optimal power flow problems on the standard IEEE 30-bus and 57-bus systems to investigate its effectiveness. The objective of the optimal power flow problem is to minimize the total fuel cost while satisfying generator operational constraints of generators, transformers, shunt capacitors and capacity of transmission lines. The results indicate that the proposed method gives better solutions than the conventional Cuckoo search algorithm and other algorithms in literature.

本文言語English
ホスト出版物のタイトル2016 IEEE International Conference on Power System Technology, POWERCON 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781467388481
DOI
出版ステータスPublished - 2016 11 22
イベント2016 IEEE International Conference on Power System Technology, POWERCON 2016 - Wollongong, Australia
継続期間: 2016 9 282016 10 1

Other

Other2016 IEEE International Conference on Power System Technology, POWERCON 2016
国/地域Australia
CityWollongong
Period16/9/2816/10/1

ASJC Scopus subject areas

  • 再生可能エネルギー、持続可能性、環境
  • 電子工学および電気工学
  • エネルギー工学および電力技術

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