Optimal placement and sizing of Static Var Compensator using Cuckoo search algorithm

Khai Phuc Nguyen; Goro Fujita; Vo Ngoc Dieu

doi:10.1109/CEC.2015.7256901

Optimal placement and sizing of Static Var Compensator using Cuckoo search algorithm

Khai Phuc Nguyen, Goro Fujita, Vo Ngoc Dieu

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

This paper presents an application of Cuckoo search algorithm to determine location and sizing of Static Var Compensator (SVC). The objective of this problem is to reconfigure the electric power system by installing SVC devices. The objective function considers loss power, voltage deviation and operational cost of SVC and other operating constraints in power system. Cuckoo search algorithm is evaluated on two case studies and compared with the Teaching-learning-based optimization method. The results show that Cuckoo search algorithm is better than the Teaching-learning-based optimization and its performance is also better.

本文言語	English
ホスト出版物のタイトル	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
出版社	Institute of Electrical and Electronics Engineers Inc.
ページ	267-274
ページ数	8
ISBN（印刷版）	9781479974924
DOI	https://doi.org/10.1109/CEC.2015.7256901
出版ステータス	Published - 2015 9 10
イベント	IEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan 継続期間: 2015 5 25 → 2015 5 28

Other

Other	IEEE Congress on Evolutionary Computation, CEC 2015
国/地域	Japan
City	Sendai
Period	15/5/25 → 15/5/28

ASJC Scopus subject areas

コンピュータサイエンスの応用
計算数学

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10.1109/CEC.2015.7256901

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引用スタイル

Nguyen, KP, Fujita, G & Ngoc Dieu, V 2015, Optimal placement and sizing of Static Var Compensator using Cuckoo search algorithm. in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings., 7256901, Institute of Electrical and Electronics Engineers Inc., pp. 267-274, IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, 15/5/25. https://doi.org/10.1109/CEC.2015.7256901

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title = "Optimal placement and sizing of Static Var Compensator using Cuckoo search algorithm",

abstract = "This paper presents an application of Cuckoo search algorithm to determine location and sizing of Static Var Compensator (SVC). The objective of this problem is to reconfigure the electric power system by installing SVC devices. The objective function considers loss power, voltage deviation and operational cost of SVC and other operating constraints in power system. Cuckoo search algorithm is evaluated on two case studies and compared with the Teaching-learning-based optimization method. The results show that Cuckoo search algorithm is better than the Teaching-learning-based optimization and its performance is also better.",

keywords = "Cuckoo Search Algorithm, optimal placement and sizing, Static Var Compensator, Teaching-learning-based optimization",

author = "Nguyen, {Khai Phuc} and Goro Fujita and {Ngoc Dieu}, Vo",

year = "2015",

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doi = "10.1109/CEC.2015.7256901",

language = "English",

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booktitle = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",

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AU - Fujita, Goro

AU - Ngoc Dieu, Vo

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N2 - This paper presents an application of Cuckoo search algorithm to determine location and sizing of Static Var Compensator (SVC). The objective of this problem is to reconfigure the electric power system by installing SVC devices. The objective function considers loss power, voltage deviation and operational cost of SVC and other operating constraints in power system. Cuckoo search algorithm is evaluated on two case studies and compared with the Teaching-learning-based optimization method. The results show that Cuckoo search algorithm is better than the Teaching-learning-based optimization and its performance is also better.

AB - This paper presents an application of Cuckoo search algorithm to determine location and sizing of Static Var Compensator (SVC). The objective of this problem is to reconfigure the electric power system by installing SVC devices. The objective function considers loss power, voltage deviation and operational cost of SVC and other operating constraints in power system. Cuckoo search algorithm is evaluated on two case studies and compared with the Teaching-learning-based optimization method. The results show that Cuckoo search algorithm is better than the Teaching-learning-based optimization and its performance is also better.

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KW - Static Var Compensator

KW - Teaching-learning-based optimization

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BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

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Optimal placement and sizing of Static Var Compensator using Cuckoo search algorithm

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