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

2 引用（Scopus）

抜粋

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.

元の言語	English
ホスト出版物のタイトル	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
出版者	Institute of Electrical and Electronics Engineers Inc.
ページ	267-274
ページ数	8
ISBN（印刷物）	9781479974924
DOI	https://doi.org/10.1109/CEC.2015.7256901
出版物ステータス	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	Japan
市	Sendai
期間	15/5/25 → 15/5/28

フィンガープリント

ASJC Scopus subject areas

Computer Science Applications
Computational Mathematics

これを引用

Nguyen, K. P., Fujita, G., & Ngoc Dieu, V. (2015). Optimal placement and sizing of Static Var Compensator using Cuckoo search algorithm. ： 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 267-274). [7256901] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2015.7256901

Nguyen, KP, Fujita, G & Ngoc Dieu, V 2015, Optimal placement and sizing of Static Var Compensator using Cuckoo search algorithm. ： 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",

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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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文献にアクセス

10.1109/CEC.2015.7256901