Optimal power flow using self-learning cuckoo search algorithm

Khai Phuc Nguyen, Goro Fujita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Power System Technology, POWERCON 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467388481
DOIs
Publication statusPublished - 2016 Nov 22
Event2016 IEEE International Conference on Power System Technology, POWERCON 2016 - Wollongong, Australia
Duration: 2016 Sep 282016 Oct 1

Other

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

Fingerprint

Electric power systems
Electric lines
Teaching
Capacitors
Costs

Keywords

  • Cuckoo Search Algorithm
  • load change tap setting
  • Optimal power flow
  • shunt capacitors
  • voltage profile

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Energy Engineering and Power Technology

Cite this

Nguyen, K. P., & Fujita, G. (2016). Optimal power flow using self-learning cuckoo search algorithm. In 2016 IEEE International Conference on Power System Technology, POWERCON 2016 [7753986] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/POWERCON.2016.7753986

Optimal power flow using self-learning cuckoo search algorithm. / Nguyen, Khai Phuc; Fujita, Goro.

2016 IEEE International Conference on Power System Technology, POWERCON 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7753986.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Nguyen, KP & Fujita, G 2016, Optimal power flow using self-learning cuckoo search algorithm. in 2016 IEEE International Conference on Power System Technology, POWERCON 2016., 7753986, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE International Conference on Power System Technology, POWERCON 2016, Wollongong, Australia, 16/9/28. https://doi.org/10.1109/POWERCON.2016.7753986
Nguyen KP, Fujita G. Optimal power flow using self-learning cuckoo search algorithm. In 2016 IEEE International Conference on Power System Technology, POWERCON 2016. Institute of Electrical and Electronics Engineers Inc. 2016. 7753986 https://doi.org/10.1109/POWERCON.2016.7753986
Nguyen, Khai Phuc ; Fujita, Goro. / Optimal power flow using self-learning cuckoo search algorithm. 2016 IEEE International Conference on Power System Technology, POWERCON 2016. Institute of Electrical and Electronics Engineers Inc., 2016.
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