Self-Learning Cuckoo search algorithm for optimal power flow considering tie-line constraints in large-scale systems

Khai Phuc Nguyen, Goro Fujita

研究成果: Article査読

3 被引用数 (Scopus)

抄録

This study proposes the Self-Learning Cuckoo search algorithm (SLCSA) and applies it for solving optimal power flow problems in large-scale power systems. The proposed method is an improvement of the Cuckoo search algorithm by employing a new strategy to focus Cuckoo eggs on the global optima. Cuckoo eggs have to learn and modify themselves to enhance their performance. The learning strategy of Cuckoo eggs is also controlled by a learning factor to prevent the search engine falling into local optima. The proposed method is applied for solving optimal power flow problems to figure the effectiveness out. The aim of the problem is to determine the minimized fuel cost while satisfying equal and unequal operating constraints of elements. The proposed SLCSA is also evaluated the problem on three IEEE 57-, 118- and 300-bus systems. According to numerical results, the proposed method is more efficient than the conventional Cuckoo search algorithm and other compared algorithms in literature.

本文言語English
ページ(範囲)118-126
ページ数9
ジャーナルGMSARN International Journal
12
2
出版ステータスPublished - 2018 6 1

ASJC Scopus subject areas

  • 管理、モニタリング、政策と法律
  • 環境科学(その他)
  • 再生可能エネルギー、持続可能性、環境
  • エネルギー工学および電力技術

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