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

Khai Phuc Nguyen, Goro Fujita

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)118-126
Number of pages9
JournalGMSARN International Journal
Volume12
Issue number2
Publication statusPublished - 2018 Jun 1

Fingerprint

Large scale systems
learning
egg
Search engines
engine
cost
method
Costs

Keywords

  • Cuckoo search algorithm
  • Optimal power flow
  • Teaching-learning based optimization
  • Tie-line constraints

ASJC Scopus subject areas

  • Management, Monitoring, Policy and Law
  • Environmental Science (miscellaneous)
  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology

Cite this

Self-Learning Cuckoo search algorithm for optimal power flow considering tie-line constraints in large-scale systems. / Nguyen, Khai Phuc; Fujita, Goro.

In: GMSARN International Journal, Vol. 12, No. 2, 01.06.2018, p. 118-126.

Research output: Contribution to journalArticle

@article{bf119a101a8d461695e7c19965b8b964,
title = "Self-Learning Cuckoo search algorithm for optimal power flow considering tie-line constraints in large-scale systems",
abstract = "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.",
keywords = "Cuckoo search algorithm, Optimal power flow, Teaching-learning based optimization, Tie-line constraints",
author = "Nguyen, {Khai Phuc} and Goro Fujita",
year = "2018",
month = "6",
day = "1",
language = "English",
volume = "12",
pages = "118--126",
journal = "GMSARN International Journal",
issn = "1905-9094",
publisher = "Greater Mekong Subregion Academic and Research Network, Asian Institute of Technology",
number = "2",

}

TY - JOUR

T1 - Self-Learning Cuckoo search algorithm for optimal power flow considering tie-line constraints in large-scale systems

AU - Nguyen, Khai Phuc

AU - Fujita, Goro

PY - 2018/6/1

Y1 - 2018/6/1

N2 - 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.

AB - 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.

KW - Cuckoo search algorithm

KW - Optimal power flow

KW - Teaching-learning based optimization

KW - Tie-line constraints

UR - http://www.scopus.com/inward/record.url?scp=85047908326&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85047908326&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:85047908326

VL - 12

SP - 118

EP - 126

JO - GMSARN International Journal

JF - GMSARN International Journal

SN - 1905-9094

IS - 2

ER -