A multi-object black hole gravitational search algorithm for day-ahead reactive optimization in distribution network

Hongyan Pei; Yunhua Li; Keyan Liu

doi:10.1109/CGNCC.2016.7828905

A multi-object black hole gravitational search algorithm for day-ahead reactive optimization in distribution network

Hongyan Pei
, Yunhua Li^*
, Keyan Liu

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Heuristic search algorithms, based on their advantages in multi-objective and multi-dimensions optimization, have been widely utilized in reactive power optimization problem. In this paper, we propose a new heuristic search algorithm, named black hole gravitational search algorithm (BHGSA), which is combined black hole algorithm (BHA) with gravitational search algorithm (GSA), to solve day-ahead reactive power optimization problem in distribution network. The simulation result to solve the day-ahead reactive power optimization problem of IEEE 33-bus distribution network system with wind generators and photovoltaic generator using BHGSA is provided. By compared with traditional gravitational search algorithm, it presents advantages in global searching ability and avoiding early convergence.

Original language	English
Title of host publication	CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	901-906
Number of pages	6
ISBN (Electronic)	9781467383189
DOIs	https://doi.org/10.1109/CGNCC.2016.7828905
State	Published - 20 Jan 2017
Event	7th IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2016 - Nanjing, Jiangsu, China Duration: 12 Aug 2016 → 14 Aug 2016

Publication series

Name	CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference

Conference

Conference	7th IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2016
Country/Territory	China
City	Nanjing, Jiangsu
Period	12/08/16 → 14/08/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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10.1109/CGNCC.2016.7828905

Cite this

Pei, H., Li, Y., & Liu, K. (2017). A multi-object black hole gravitational search algorithm for day-ahead reactive optimization in distribution network. In CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference (pp. 901-906). Article 7828905 (CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CGNCC.2016.7828905

Pei, Hongyan ; Li, Yunhua ; Liu, Keyan. / A multi-object black hole gravitational search algorithm for day-ahead reactive optimization in distribution network. CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 901-906 (CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference).

@inproceedings{6795d5d19a2548b785a8e2e7923fd09a,

title = "A multi-object black hole gravitational search algorithm for day-ahead reactive optimization in distribution network",

abstract = "Heuristic search algorithms, based on their advantages in multi-objective and multi-dimensions optimization, have been widely utilized in reactive power optimization problem. In this paper, we propose a new heuristic search algorithm, named black hole gravitational search algorithm (BHGSA), which is combined black hole algorithm (BHA) with gravitational search algorithm (GSA), to solve day-ahead reactive power optimization problem in distribution network. The simulation result to solve the day-ahead reactive power optimization problem of IEEE 33-bus distribution network system with wind generators and photovoltaic generator using BHGSA is provided. By compared with traditional gravitational search algorithm, it presents advantages in global searching ability and avoiding early convergence.",

author = "Hongyan Pei and Yunhua Li and Keyan Liu",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 7th IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2016 ; Conference date: 12-08-2016 Through 14-08-2016",

year = "2017",

month = jan,

day = "20",

doi = "10.1109/CGNCC.2016.7828905",

language = "英语",

series = "CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "901--906",

booktitle = "CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference",

address = "美国",

}

Pei, H, Li, Y & Liu, K 2017, A multi-object black hole gravitational search algorithm for day-ahead reactive optimization in distribution network. in CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference., 7828905, CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference, Institute of Electrical and Electronics Engineers Inc., pp. 901-906, 7th IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2016, Nanjing, Jiangsu, China, 12/08/16. https://doi.org/10.1109/CGNCC.2016.7828905

A multi-object black hole gravitational search algorithm for day-ahead reactive optimization in distribution network. / Pei, Hongyan; Li, Yunhua; Liu, Keyan.
CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference. Institute of Electrical and Electronics Engineers Inc., 2017. p. 901-906 7828905 (CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A multi-object black hole gravitational search algorithm for day-ahead reactive optimization in distribution network

AU - Pei, Hongyan

AU - Li, Yunhua

AU - Liu, Keyan

PY - 2017/1/20

Y1 - 2017/1/20

N2 - Heuristic search algorithms, based on their advantages in multi-objective and multi-dimensions optimization, have been widely utilized in reactive power optimization problem. In this paper, we propose a new heuristic search algorithm, named black hole gravitational search algorithm (BHGSA), which is combined black hole algorithm (BHA) with gravitational search algorithm (GSA), to solve day-ahead reactive power optimization problem in distribution network. The simulation result to solve the day-ahead reactive power optimization problem of IEEE 33-bus distribution network system with wind generators and photovoltaic generator using BHGSA is provided. By compared with traditional gravitational search algorithm, it presents advantages in global searching ability and avoiding early convergence.

AB - Heuristic search algorithms, based on their advantages in multi-objective and multi-dimensions optimization, have been widely utilized in reactive power optimization problem. In this paper, we propose a new heuristic search algorithm, named black hole gravitational search algorithm (BHGSA), which is combined black hole algorithm (BHA) with gravitational search algorithm (GSA), to solve day-ahead reactive power optimization problem in distribution network. The simulation result to solve the day-ahead reactive power optimization problem of IEEE 33-bus distribution network system with wind generators and photovoltaic generator using BHGSA is provided. By compared with traditional gravitational search algorithm, it presents advantages in global searching ability and avoiding early convergence.

UR - https://www.scopus.com/pages/publications/85015167890

U2 - 10.1109/CGNCC.2016.7828905

DO - 10.1109/CGNCC.2016.7828905

M3 - 会议稿件

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T3 - CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference

SP - 901

EP - 906

BT - CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th IEEE Chinese Guidance, Navigation and Control Conference, CGNCC 2016

Y2 - 12 August 2016 through 14 August 2016

ER -

Pei H, Li Y, Liu K. A multi-object black hole gravitational search algorithm for day-ahead reactive optimization in distribution network. In CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference. Institute of Electrical and Electronics Engineers Inc. 2017. p. 901-906. 7828905. (CGNCC 2016 - 2016 IEEE Chinese Guidance, Navigation and Control Conference). doi: 10.1109/CGNCC.2016.7828905

A multi-object black hole gravitational search algorithm for day-ahead reactive optimization in distribution network

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